05_niches_SIBER_stomach_contents
Peter Flood
2024-03-04
1 Load Libraries
library(viridis)
Warning: package ‘viridis’ was built under R version 4.2.3Loading required package: viridisLite
Warning: package ‘viridisLite’ was built under R version 4.2.3Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#Post-Invasion ## Load data This file is output from 03_diets_from_stomach_contents.Rmd
post.consumer.nmds <- read.csv("Outputs/Stomach Contents/Diet/Post-Invasion/post_nmds_consumers.csv")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.1 Format data for SIBER package
#create object that has dropped groups (not all rows that were dropped)
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentremoved.consumers <- stomach.habitat.season[!(stomach.habitat.season$group %in% siber.data$group),]Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentCreate SIBER object, example sample sizes
#createSiberObjectError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentglobal.siber <- createSiberObject(siber.data)Warning: At least one of your groups has less than 5 observations.
The absolute minimum sample size for each group is 3 in order
for the various ellipses and corresponding metrics to be
calculated. More reasonably though, a minimum of 5 data points
are required to calculate the two means and the 2x2 covariance
matrix and not run out of degrees of freedom. Check the item
named 'sample.sizes' in the object returned by this function
in order to locate the offending group. Bear in mind that NAs in
the sample.size matrix simply indicate groups that are not
present in that community, and is an acceptable data structure
for these analyses.Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write out table of sample sizesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(global.siber[["sample.sizes"]] %>% t(), "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/siber_global_n_table_stomachs.csv")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentVisualize SIBER object
# Create lists of plotting arguments to be passed onwards to each Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# of the three plotting functions.Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcommunity.hulls.args <- list(col = 1, lty = 1, lwd = 1)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentgroup.ellipses.args <- list(n = 100, p.interval = 0.95, lty = 1, lwd = 2)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentgroup.hull.args <- list(lty = 2, col = "grey20")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpar(mfrow=c(1,1))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentplotSiberObject(global.siber,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ax.pad = 2, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument hulls = F, community.hulls.args, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ellipses = T, group.ellipses.args,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument group.hulls = T, group.hull.args,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument bty = "L",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument iso.order = c(1,2),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument xlab = expression(d13C),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ylab = expression(d15N)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
1.2 Calculate TA, SEA, and SEAc
global.ML <- groupMetricsML(global.siber)Warning: NaNs producedWarning: NaNs producedWarning: NaNs producedError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentprint(global.ML) SRS_Marsh_Dry.C.uro1 SRS_Marsh_Dry.E.eve1 SRS_Marsh_Dry.E.glo1 SRS_Marsh_Dry.E.suc1 SRS_Marsh_Dry.F.chr1 SRS_Marsh_Dry.G.hol1
TA 0.001252622 0.07558610 0.4095575 0.1485589 0.9982900 0.04252190
SEA 0.001556821 0.04206251 0.3824250 0.1952394 0.4862619 0.03502125
SEAc 0.002335232 0.04907293 0.5099000 0.2928591 0.5557279 0.04377656
SRS_Marsh_Dry.G.hol2 SRS_Marsh_Dry.H.for2 SRS_Marsh_Dry.H.let1 SRS_Marsh_Dry.H.lit1 SRS_Marsh_Dry.L.goo1 SRS_Marsh_Dry.L.goo2
TA 0.008647373 0.01414082 1.9475214 0.006322043 0.011644541 0.8230279
SEA 0.010752746 0.01358786 0.5513666 0.011466918 0.005598491 0.1549533
SEAc 0.014336995 0.01811715 0.5691526 0.022933836 0.005948396 0.1616904
SRS_Marsh_Dry.L.mar1 SRS_Marsh_Dry.L.pun2 SRS_Marsh_Dry.R.gry1 SRS_Marsh_Dry.S.lac1 SRS_Marsh_Wet.C.bat1 SRS_Marsh_Wet.C.und1
TA 0.1323360 0.6730353 0.2634321 0.02923107 1.4593357 0.1800839
SEA 0.2400309 0.4936603 0.2181587 0.05301930 0.8567919 0.3266361
SEAc 0.4800619 0.5923924 0.2726983 0.10603861 0.9995906 0.6532723
SRS_Marsh_Wet.C.uro1 SRS_Marsh_Wet.C.uro2 SRS_Marsh_Wet.E.suc1 SRS_Marsh_Wet.F.chr1 SRS_Marsh_Wet.G.hol1 SRS_Marsh_Wet.G.hol2
TA 0.004475145 0.03129033 0.04705993 1.8137478 0.2908201 1.2811412
SEA 0.001911445 0.04031549 0.08535727 0.6932349 0.1106854 0.4015693
SEAc 0.002058480 0.06047323 0.17071454 0.7510044 0.1176033 0.4266674
SRS_Marsh_Wet.H.for1 SRS_Marsh_Wet.H.for2 SRS_Marsh_Wet.H.let1 SRS_Marsh_Wet.J.flo1 SRS_Marsh_Wet.L.goo1 SRS_Marsh_Wet.L.goo2
TA 0.3878965 0.06757812 0.8646135 0.000000e+00 0.29551255 0.5397746
SEA 0.2049944 0.06452674 0.2038337 1.291239e-09 0.08878240 0.2004657
SEAc 0.2391601 0.08603566 0.2130989 2.582479e-09 0.09512401 0.2147846
SRS_Marsh_Wet.L.mac1 SRS_Marsh_Wet.L.mic1 SRS_Marsh_Wet.L.mic2 SRS_Marsh_Wet.L.pun2 SRS_Marsh_Wet.O.aur1 SRS_NP_Dry.F.chr1 SRS_NP_Dry.G.hol1
TA 1.8107905 7.689884e-06 6.552195e-05 0.04418207 0.4336553 2.158534 0.0015269157
SEA 0.7723108 1.394791e-05 1.188437e-04 0.08013742 0.2206457 1.825474 0.0009501055
SEAc 0.8237982 2.789581e-05 2.376873e-04 0.16027484 0.2482264 2.433965 0.0010858349
SRS_NP_Dry.G.hol2 SRS_NP_Dry.H.for1 SRS_NP_Dry.H.for2 SRS_NP_Dry.L.goo1 SRS_NP_Dry.L.goo2 SRS_NP_Dry.P.lat2 SRS_NP_Wet.E.eve1 SRS_NP_Wet.G.hol1
TA 0.13185518 9.420281e-05 1.032404e-04 0.0007884438 0.09856447 0 1.7528014 0.02519141
SEA 0.06568541 1.279538e-04 9.456322e-05 0.0005351115 0.08525426 0 0.6056994 0.03730750
SEAc 0.07298379 1.919306e-04 1.260843e-04 0.0006421338 0.10230511 0 0.6413288 0.05596125
SRS_NP_Wet.G.hol2 SRS_NP_Wet.H.for1 SRS_NP_Wet.H.for2 SRS_Pond_Dry.A.nat1 SRS_Pond_Dry.A.nat2 SRS_Pond_Dry.A.nat3 SRS_Pond_Dry.A.nat4
TA 0.03538461 0.2345029 0.1015890 0.08307403 0 0.2497199 0.3770075
SEA 0.06418058 0.2016806 0.1280374 0.15067962 NaN 0.2339349 0.2340619
SEAc 0.12836117 0.2521007 0.1707165 0.30135923 NaN 0.2924187 0.2730722
SRS_Pond_Dry.E.suc2 SRS_Pond_Dry.F.chr1 SRS_Pond_Dry.G.hol1 SRS_Pond_Dry.G.hol2 SRS_Pond_Dry.H.for1 SRS_Pond_Dry.H.for2 SRS_Pond_Dry.H.lit1
TA 6.938894e-18 1.5234316 1.5150495 2.8457383 0.1301756 0.02196943 0.04053378
SEA NaN 0.7789338 0.6525182 0.9941296 0.1482638 0.03086624 0.03150864
SEAc NaN 0.8654820 0.6991267 1.0338948 0.1976851 0.04115499 0.03676008
SRS_Pond_Dry.J.flo2 SRS_Pond_Dry.L.goo1 SRS_Pond_Dry.L.goo2 SRS_Pond_Dry.L.gul2 SRS_Pond_Dry.L.gul3 SRS_Pond_Dry.L.gul4 SRS_Pond_Dry.L.mar1
TA 0.3715855 0.2741502 0.6581333 0.6116540 0.8093715 0.07908698 0.5000114
SEA 0.6739815 0.1051434 0.1911146 0.3431941 0.5125961 0.11712473 0.3954521
SEAc 1.3479631 0.1104006 0.2002153 0.3922218 0.5858241 0.17568709 0.4745425
SRS_Pond_Dry.L.pla1 SRS_Pond_Dry.L.pun2 SRS_Pond_Dry.P.lat2 SRS_Pond_Wet.A.cal2 SRS_Pond_Wet.C.uro1 SRS_Pond_Wet.E.eve1 SRS_Pond_Wet.E.glo1
TA 0.8175539 0.2293426 0.2339015 0.07295236 0.001541911 0.6388393 5.344319e-05
SEA 0.5722196 0.3396474 0.2939293 0.13232094 0.001639758 0.4302703 9.693522e-05
SEAc 0.6357995 0.5094712 0.3674117 0.26464187 0.002049698 0.5163244 1.938704e-04
SRS_Pond_Wet.E.suc1 SRS_Pond_Wet.F.chr1 SRS_Pond_Wet.G.hol1 SRS_Pond_Wet.G.hol2 SRS_Pond_Wet.H.for1 SRS_Pond_Wet.H.for2 SRS_Pond_Wet.H.let1
TA 0.02455418 1.5388886 0.7971429 1.1819669 0.1658047 0.03604592 0.18821506
SEA 0.02579583 0.4646445 0.3268040 0.3905955 0.0934247 0.06538007 0.06786097
SEAc 0.03439444 0.4867704 0.3472293 0.4111531 0.1067711 0.13076014 0.07351606
SRS_Pond_Wet.L.goo1 SRS_Pond_Wet.L.goo2 SRS_Pond_Wet.L.gul2 SRS_Pond_Wet.L.gul3 SRS_Pond_Wet.L.gul4 SRS_Pond_Wet.L.mac1 SRS_Pond_Wet.L.mic2
TA 0.26551749 1.2386880 0.7817562 0.9771948 0.2131265 0.6200728 0
SEA 0.08619423 0.3184621 1.1281235 0.7274734 0.3865686 0.5201127 0
SEAc 0.09098280 0.3371952 1.6921852 0.8313982 0.7731373 0.6501409 0
SRS_Pond_Wet.L.pla1 SRS_Pond_Wet.L.pla2 SRS_Pond_Wet.L.pun2 SRS_Pond_Wet.M.sal1 SRS_Pond_Wet.M.sia1 SRS_Pond_Wet.O.aur1 SRS_Pond_Wet.P.lat1
TA 1.7135690 0.06334676 0.5572423 0.3196648 0.000000e+00 0.1677636 0.000000e+00
SEA 0.9165857 0.09381409 0.3998694 0.3642255 1.359580e-08 0.2484514 1.224977e-09
SEAc 0.9999116 0.14072113 0.4798433 0.4370706 1.699475e-08 0.3726771 1.633303e-09
SRS_Pond_Wet.P.lat2 SRS_Pond_Wet.T.mar1 TSL_Marsh_Dry.E.glo1 TSL_Marsh_Dry.F.chr1 TSL_Marsh_Dry.H.for1 TSL_Marsh_Dry.H.for2
TA 0.08263205 0 0.5403473 0.4887086 0.03526574 0.2905959
SEA 0.08292497 NaN 0.2758183 0.4128811 0.01902016 0.1629532
SEAc 0.09674580 NaN 0.3152209 0.5505081 0.02074926 0.1833223
TSL_Marsh_Dry.L.goo1 TSL_Marsh_Dry.L.goo2 TSL_Marsh_Wet.F.chr1 TSL_Marsh_Wet.G.hol1 TSL_Marsh_Wet.G.hol2 TSL_Marsh_Wet.H.for1
TA 0.014924198 0.15939345 1.6110500 0.5668184 1.5168403 0.3329676
SEA 0.003614567 0.04058646 0.7359352 0.2265175 0.5401846 0.2035400
SEAc 0.003778866 0.04220992 0.8279271 0.2416186 0.5817373 0.2261556
TSL_Marsh_Wet.H.for2 TSL_Marsh_Wet.L.goo1 TSL_Marsh_Wet.L.goo2 TSL_NP_Wet.C.bim1 TSL_NP_Wet.E.eve1 TSL_NP_Wet.G.hol1 TSL_NP_Wet.H.for1
TA 0.1301798 0.3416347 0.4642188 0.2703017 0.0001549936 0.5346954 0.2612340
SEA 0.1290492 0.1234875 0.1470044 0.4902731 0.0002811273 0.4143179 0.1468574
SEAc 0.1720656 0.1312055 0.1568047 0.9805461 0.0005622547 0.4971815 0.1631748
TSL_NP_Wet.H.for2 TSL_NP_Wet.L.goo1 TSL_NP_Wet.L.goo2 TSL_Pond_Dry.G.hol1 TSL_Pond_Dry.G.hol2 TSL_Pond_Dry.H.for2 TSL_Pond_Dry.L.goo1
TA 0.07713746 0.11223239 0.05911680 1.2892784 2.9237806 0.007759673 0.005750619
SEA 0.09398721 0.06462386 0.06167987 0.4663795 0.9054493 0.014074491 0.006689370
SEAc 0.14098081 0.07385584 0.08223983 0.4858119 0.9485659 0.028148981 0.010034055
TSL_Pond_Dry.L.goo2 TSL_Pond_Dry.M.ber1 TSL_Pond_Dry.N.pet1 TSL_Pond_Wet.G.hol1 TSL_Pond_Wet.G.hol2 TSL_Pond_Wet.H.for2 TSL_Pond_Wet.L.goo1
TA 0.3423274 0.1106508 1.5372695 0.5360989 1.7281736 0.008241115 0.4166400
SEA 0.1596070 0.2006984 0.6416958 0.2127115 0.6643605 0.014947730 0.1414431
SEAc 0.1824079 0.4013968 0.6910570 0.2320489 0.7034405 0.029895459 0.1493011
TSL_Pond_Wet.L.goo2 TSL_Pond_Wet.L.mac1 TSL_Pond_Wet.L.sic1 TSL_Pond_Wet.M.sal1 TSL_Pond_Wet.M.sia1 TSL_Pond_Wet.N.pet1 TSL_Pond_Wet.T.mar1
TA 0.9259922 0.8792575 0.6682930 3.039955 0.2004722 0.06686001 0.6368588
SEA 0.2809845 0.8134562 0.2104222 1.096649 0.1860558 0.03691730 0.2675803
SEAc 0.2965947 1.0846083 0.2204423 1.165190 0.2067287 0.04153196 0.3010278Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(t(global.ML), file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/all_spp_ML_stomachs.csv")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.3 Calculate Layman metrics
#calculate Layman metrics
post.layman.metrics <- communityMetricsML(global.siber)1.4 SIBER model
Set up model parameters
#set up the model parameters Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# options for running jagsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentparms <- list()Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentparms$n.iter <- 2 * 10^6 # number of iterations to run the model forError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentparms$n.burnin <- 1 * 10^4 # discard the first set of valuesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentparms$n.thin <- 100 # thin the posterior by this manyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentparms$n.chains <- 2 # run this many chainsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentparms$save.output = TError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentparms$save.dir = getwd()Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# define the priorsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpriors <- list()Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpriors$R <- 1 * diag(2)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpriors$k <- 2Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpriors$tau.mu <- 1.0E-3Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentRun SIBER model
# fit the ellipses which uses an Inverse Wishart priorError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# on the covariance matrix Sigma, and a vague normal prior on the Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# means. Fitting is via the JAGS method.Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentglobal.ellipses.posterior <- siberMVN(global.siber, parms, priors)Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 33
Unobserved stochastic nodes: 3
Total graph size: 48
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|*********************************************** | 94%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
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|******** | 16%
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|
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|
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|
|************************ | 48%
|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|************************************ | 72%
|
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|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
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|
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|
|******************************************** | 88%
|
|********************************************* | 90%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********** | 20%
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|************ | 24%
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|***************** | 34%
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|
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|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********* | 18%
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|********** | 20%
|
|*********** | 22%
|
|************ | 24%
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|************* | 26%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 15
Unobserved stochastic nodes: 3
Total graph size: 30
Initializing model
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|**** | 8%
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
|
|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
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|***** | 10%
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
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|****** | 12%
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
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|
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|
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|
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|
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|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 24
Unobserved stochastic nodes: 3
Total graph size: 39
Initializing model
|
| | 0%
|
|* | 2%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
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|****** | 12%
|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
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|
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|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|**************** | 32%
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|
|******************** | 40%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
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|
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|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
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|********* | 18%
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|********** | 20%
|
|*********** | 22%
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|************* | 26%
|
|************** | 28%
|
|*************** | 30%
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|**************** | 32%
|
|***************** | 34%
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|****************** | 36%
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|******************** | 40%
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|********************* | 42%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|********* | 18%
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|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
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|************** | 28%
|
|*************** | 30%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
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|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
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|
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|
|********************* | 42%
|
|********************** | 44%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|**** | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|**** | 8%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
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|
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|
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|
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|
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|
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
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|
|************************************ | 72%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
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|
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|
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|
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|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|****** | 12%
|
|******* | 14%
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|
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|
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|
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|
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|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
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|
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|
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|
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|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
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|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
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|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
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|
|****************** | 36%
|
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|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 19
Unobserved stochastic nodes: 3
Total graph size: 34
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
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|********** | 20%
|
|************ | 24%
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|************* | 26%
|
|************** | 28%
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|*************** | 30%
|
|**************** | 32%
|
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|
|****************** | 36%
|
|******************** | 40%
|
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|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
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|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
|************************************ | 72%
|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|**** | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|******************************** | 64%
|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 27
Unobserved stochastic nodes: 3
Total graph size: 42
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******** | 16%
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|
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|
|*********************** | 46%
|
|************************ | 48%
|
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|
|************************** | 52%
|
|*************************** | 54%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|
|****************************************** | 84%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********** | 20%
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|************ | 24%
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|
|*************** | 30%
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|**************** | 32%
|
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|****************** | 36%
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|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
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|****************************************** | 84%
|
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|******************************************** | 88%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
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|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
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|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
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|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
|*********************************************** | 94%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|**** | 8%
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|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|****** | 12%
|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
|*********************************************** | 94%
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|******* | 14%
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|******** | 16%
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|*************** | 30%
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|
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|
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|
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|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|*********************************** | 70%
|
|************************************ | 72%
|
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|
|************************************** | 76%
|
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|
|**************************************** | 80%
|
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|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|************************ | 48%
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|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|****************************************** | 84%
|
|******************************************* | 86%
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|******************************************** | 88%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
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|******** | 16%
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|
|*********************** | 46%
|
|************************ | 48%
|
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|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|******************************************* | 86%
|
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|************************************************ | 96%
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|********************************* | 66%
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|
|************************************ | 72%
|
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|
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|
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|****************************************** | 84%
|
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|
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|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|********* | 18%
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|
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|**************** | 32%
|
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|
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|
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|
|********************* | 42%
|
|********************** | 44%
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|
|************************ | 48%
|
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|
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|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|**************************************** | 80%
|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********** | 20%
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|
|*************** | 30%
|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 21
Unobserved stochastic nodes: 3
Total graph size: 36
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|******************************** | 64%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|****** | 12%
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|
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|
|************************ | 48%
|
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|
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|
|**************************** | 56%
|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************** | 68%
|
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|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
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|
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Initializing model
|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|***** | 10%
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|****** | 12%
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|
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|
|************************ | 48%
|
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|
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|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|****************************************** | 84%
|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 20
Unobserved stochastic nodes: 3
Total graph size: 35
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 19
Unobserved stochastic nodes: 3
Total graph size: 34
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
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|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|****** | 12%
|
|******* | 14%
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
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|
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|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
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|
|********************************************* | 90%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|
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|
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|
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|
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|
|**************** | 32%
|
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|
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|
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|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
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|
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|
|*************************** | 54%
|
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|
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|
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|
|******************************* | 62%
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
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|* | 2%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
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|* | 2%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 24
Unobserved stochastic nodes: 3
Total graph size: 39
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|******************************** | 64%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 27
Unobserved stochastic nodes: 3
Total graph size: 42
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|******************************** | 64%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 15
Unobserved stochastic nodes: 3
Total graph size: 30
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
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|* | 2%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
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|* | 2%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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|******************************** | 64%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|************************************* | 74%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|
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|
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|
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|******************************************** | 88%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 26
Unobserved stochastic nodes: 3
Total graph size: 41
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|* | 2%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 15
Unobserved stochastic nodes: 3
Total graph size: 30
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 19
Unobserved stochastic nodes: 3
Total graph size: 34
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|******************************** | 64%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 20
Unobserved stochastic nodes: 3
Total graph size: 35
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|*************** | 30%
|
|**************** | 32%
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|
|****************************************** | 84%
|
|******************************************* | 86%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 20
Unobserved stochastic nodes: 3
Total graph size: 35
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|********* | 18%
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|**************** | 32%
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|********************** | 44%
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
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|************************************* | 74%
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|************************************** | 76%
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|**************************************** | 80%
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|****************************************** | 84%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|
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Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
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Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
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|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
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Compiling model graph
Resolving undeclared variables
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Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
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|**************************************************| 100%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#calculate posterior estimates of ellipses for all groupsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentSEA.B<-siberEllipses(global.ellipses.posterior)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(SEA.B)<-names(global.ellipses.posterior)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.1 Extract SIBER output
#calculate credibility intervalsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcr.p<-c(0.5, 0.95, 0.99) #vector of desired quantilesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#call hdrcde_hdr using lapply()Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentSEA.B.credibles<-lapply(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument as.data.frame(SEA.B), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument function(x,...){tmp<-hdrcde::hdr(x)$hdr},Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument prob = cr.p)Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#match up group namesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentnames(SEA.B.credibles)<-colnames(global.ML)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#this carries through the row names from matrices in SEA.B.crediblesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#they don't turn out all that clean though, so adding a column later with the same infoError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenty <- do.call(function(...) {Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument tmp <- plyr::rbind.fill.matrix(...)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames(tmp) <- sapply(SEA.B.credibles, function(i) {Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames(i)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument })Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument return(tmp)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument}, SEA.B.credibles)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenty 1 2 3 4
99% 8.839193e-04 0.0166563532 NA NA
95% 1.112711e-03 0.0102994163 NA NA
50% 1.960522e-03 0.0041911035 NA NA
99% 3.251093e-02 0.2325574066 NA NA
95% 3.893819e-02 0.1739961597 NA NA
50% 5.931204e-02 0.0987459983 NA NA
99% 1.100113e-01 1.5282865564 NA NA
95% 1.415282e-01 1.0109733072 NA NA
50% 2.459253e-01 0.4754208665 NA NA
99% 4.269473e-02 0.9343876426 NA NA
95% 6.032146e-02 0.5788050518 NA NA
50% 1.109088e-01 0.2363608335 NA NA
99% 1.965008e-01 1.2516831088 NA NA
95% 2.334241e-01 0.9431936670 NA NA
50% 3.480625e-01 0.5618499937 NA NA
99% 2.160228e-02 0.2313712481 NA NA
95% 2.777511e-02 0.1593837429 NA NA
50% 4.474274e-02 0.0811219009 NA NA
99% 1.858066e-02 0.2485140555 0.250851349 0.259109731
95% 2.346115e-02 0.1677430242 NA NA
50% 4.030350e-02 0.0784107256 NA NA
99% 5.752697e-03 0.0776056624 NA NA
95% 7.158050e-03 0.0514874211 NA NA
50% 1.209172e-02 0.0236129380 NA NA
99% 3.463642e-01 0.8711369081 NA NA
95% 3.847476e-01 0.7700051834 NA NA
50% 4.759864e-01 0.6047084072 NA NA
99% 2.571024e-02 1.2773433254 NA NA
95% 4.456136e-02 0.7149661751 NA NA
50% 9.552952e-02 0.2356527042 NA NA
99% 3.007995e-03 0.0107374310 NA NA
95% 3.402439e-03 0.0089647180 NA NA
50% 4.508334e-03 0.0063110952 NA NA
99% 9.070060e-02 0.2658670835 NA NA
95% 1.023977e-01 0.2293640600 NA NA
50% 1.315157e-01 0.1727064823 NA NA
99% 5.294391e-02 2.3960616674 NA NA
95% 8.288121e-02 1.3064537123 NA NA
50% 1.816190e-01 0.4485545166 NA NA
99% 1.650048e-01 1.5146857370 NA NA
95% 2.061408e-01 1.0709021309 NA NA
50% 3.372567e-01 0.5843385333 NA NA
99% 7.740037e-02 0.8437848770 NA NA
95% 9.833793e-02 0.5830639616 NA NA
50% 1.616961e-01 0.2943746358 NA NA
99% 9.660495e-03 0.3403128852 NA NA
95% 1.318245e-02 0.2004723840 NA NA
50% 2.641410e-02 0.0653963613 NA NA
99% 3.217610e-01 2.3787484040 NA NA
95% 3.935075e-01 1.7632289838 NA NA
50% 6.051957e-01 1.0094967366 NA NA
99% 1.093996e-01 4.1845992340 NA NA
95% 1.535451e-01 2.3262295960 NA NA
50% 3.132035e-01 0.7825204120 NA NA
99% 1.264338e-03 0.0050725052 0.005153530 0.005234555
95% 1.426387e-03 0.0041812320 NA NA
50% 1.993561e-03 0.0028038097 NA NA
99% 5.917146e-02 1.3019305833 NA NA
95% 8.209219e-02 0.7813879939 NA NA
50% 1.509202e-01 0.3226071648 NA NA
99% 1.535377e-02 0.6798825548 NA NA
95% 2.429716e-02 0.3838890474 NA NA
50% 5.178821e-02 0.1282160084 NA NA
99% 3.298130e-01 1.4373010891 NA NA
95% 3.872898e-01 1.1643426412 NA NA
50% 5.377362e-01 0.7821689361 NA NA
99% 5.799958e-02 0.2090098718 NA NA
95% 6.697789e-02 0.1754609353 NA NA
50% 8.995472e-02 0.1249016138 NA NA
99% 2.122630e-01 0.7702026271 NA NA
95% 2.450365e-01 0.6436893146 NA NA
50% 3.307516e-01 0.4595047165 NA NA
99% 7.515220e-02 0.5706857140 NA NA
95% 9.347496e-02 0.4235278469 NA NA
50% 1.437855e-01 0.2394843406 NA NA
99% 2.106855e-02 0.2893363731 NA NA
95% 2.725958e-02 0.1936757671 NA NA
50% 4.617418e-02 0.0901624718 NA NA
99% 1.186151e-01 0.3531883265 NA NA
95% 1.332616e-01 0.3031526338 NA NA
50% 1.712900e-01 0.2266295448 NA NA
99% 1.205104e-02 0.5527638349 NA NA
95% 1.925705e-02 0.2978212164 NA NA
50% 4.061294e-02 0.1004242532 NA NA
99% 4.594164e-02 0.1757544972 NA NA
95% 5.258739e-02 0.1446149708 NA NA
50% 7.063513e-02 0.1002573897 NA NA
99% 1.024945e-01 0.3977257154 NA NA
95% 1.172152e-01 0.3277320925 NA NA
50% 1.611468e-01 0.2276616854 NA NA
99% 4.039107e-01 1.4897619682 NA NA
95% 4.600682e-01 1.2354287560 NA NA
50% 6.208453e-01 0.8712614355 NA NA
99% 3.410718e-05 0.0013585366 NA NA
95% 3.410718e-05 0.0006749577 NA NA
50% 1.134492e-04 0.0002470417 NA NA
99% 2.134596e-05 0.0008604226 NA NA
95% 2.134596e-05 0.0004731565 NA NA
50% 8.589032e-05 0.0001504347 NA NA
99% 1.065759e-01 4.6851102245 NA NA
95% 1.570896e-01 2.4797534217 NA NA
50% 3.335231e-01 0.8224520543 NA NA
99% 9.001044e-02 0.5424528599 NA NA
95% 1.103392e-01 0.4200299210 NA NA
50% 1.656192e-01 0.2602981634 NA NA
99% 5.006529e-01 6.9763550803 7.337646822 7.535801896
95% 6.444389e-01 4.7452248730 NA NA
50% 1.127166e+00 2.1950032973 NA NA
99% 4.067872e-04 0.0025473540 NA NA
95% 4.646404e-04 0.0018531162 NA NA
50% 6.960530e-04 0.0011010251 NA NA
99% 3.172719e-02 0.1675723111 NA NA
95% 3.731016e-02 0.1318289296 NA NA
50% 5.383615e-02 0.0824364867 NA NA
99% 9.420558e-05 0.0021217041 0.002121704 0.002223079
95% 9.420558e-05 0.0013107047 NA NA
50% 2.969554e-04 0.0006010802 NA NA
99% 2.221874e-05 0.0004073893 NA NA
95% 2.221874e-05 0.0002789991 NA NA
50% 6.501547e-05 0.0001292106 NA NA
99% 1.930454e-04 0.0016413302 NA NA
95% 2.429863e-04 0.0011918625 NA NA
50% 3.428680e-04 0.0006425131 NA NA
99% 6.107446e-02 0.5321633667 NA NA
95% 7.482467e-02 0.3845213777 NA NA
50% 1.203573e-01 0.2079521391 NA NA
99% 2.553740e-02 0.3621034666 NA NA
95% 3.189212e-02 0.2344527281 NA NA
50% 5.511271e-02 0.1086579041 NA NA
99% 3.232005e-01 1.1139916322 NA NA
95% 3.709676e-01 0.9458118469 NA NA
50% 4.940976e-01 0.6782371467 NA NA
99% 1.249650e-02 0.2507587558 NA NA
95% 1.627471e-02 0.1547843969 NA NA
50% 2.967403e-02 0.0635469072 NA NA
99% 3.919447e-02 1.7546322893 NA NA
95% 5.898493e-02 0.9145798658 NA NA
50% 1.252547e-01 0.3101216870 NA NA
99% 6.278786e-02 0.7216111495 NA NA
95% 8.283819e-02 0.4951433489 NA NA
50% 1.376378e-01 0.2493505431 NA NA
99% 3.439055e-02 0.4934381226 0.524983354 0.536593388
95% 4.495979e-02 0.3311336808 NA NA
50% 7.817967e-02 0.1527194265 NA NA
99% 4.457172e-02 2.1767749092 NA NA
95% 6.941818e-02 1.1144004927 NA NA
50% 1.499367e-01 0.3756204280 NA NA
99% 5.258370e-02 1.3692644587 NA NA
95% 7.945220e-02 0.8350697629 NA NA
50% 1.593452e-01 0.3397165436 NA NA
99% 6.909959e-02 0.8019100817 NA NA
95% 9.163105e-02 0.5578917477 NA NA
50% 1.552143e-01 0.2817380612 NA NA
99% 8.905987e-02 0.6562364161 NA NA
95% 1.082246e-01 0.4816415501 NA NA
50% 1.660039e-01 0.2756272570 NA NA
99% 1.888410e-02 0.7813318354 NA NA
95% 2.864166e-02 0.4349875292 NA NA
50% 5.961547e-02 0.1477695817 NA NA
99% 3.435647e-01 1.7931598987 NA NA
95% 4.060402e-01 1.4006593587 NA NA
50% 5.813109e-01 0.8902902201 NA NA
99% 3.358984e-01 1.3170819152 NA NA
95% 3.914646e-01 1.0899753097 NA NA
50% 5.344862e-01 0.7571888266 NA NA
99% 5.929663e-01 1.6710728005 NA NA
95% 6.679262e-01 1.4577663497 NA NA
50% 8.495304e-01 1.1088779092 NA NA
99% 4.238392e-02 0.6117575907 NA NA
95% 5.592127e-02 0.4073289474 NA NA
50% 9.778464e-02 0.1902540677 NA NA
99% 1.180881e-02 0.1575508512 0.160242403 0.167217520
95% 1.512596e-02 0.1067134962 NA NA
50% 2.589587e-02 0.0499621053 NA NA
99% 1.315768e-02 0.0962170840 NA NA
95% 1.571357e-02 0.0712639777 NA NA
50% 2.407211e-02 0.0401821786 NA NA
99% 7.597366e-02 4.4208563678 NA NA
95% 1.423667e-01 2.4547620090 NA NA
50% 3.412863e-01 0.8384301253 NA NA
99% 5.919363e-02 0.1876186069 NA NA
95% 6.678630e-02 0.1605154467 NA NA
50% 8.744606e-02 0.1176529618 NA NA
99% 1.093794e-01 0.3352579544 NA NA
95% 1.233102e-01 0.2863772908 NA NA
50% 1.599327e-01 0.2137816695 NA NA
99% 1.367716e-01 0.9263096917 NA NA
95% 1.687134e-01 0.6967053603 NA NA
50% 2.594214e-01 0.4167042165 NA NA
99% 2.415090e-01 1.6103821538 NA NA
95% 2.969349e-01 1.2196827040 NA NA
50% 4.476126e-01 0.7264227337 NA NA
99% 7.982698e-02 1.8057043163 NA NA
95% 1.099615e-01 1.0788167463 NA NA
50% 2.050729e-01 0.4408353654 NA NA
99% 1.354177e-01 1.1985118782 NA NA
95% 1.706487e-01 0.8637388592 NA NA
50% 2.728795e-01 0.4716115024 NA NA
99% 2.724751e-01 1.5065179900 NA NA
95% 3.302456e-01 1.1787762301 NA NA
50% 4.871176e-01 0.7435984721 NA NA
99% 7.275085e-02 1.5666789122 NA NA
95% 1.015085e-01 1.0034905691 NA NA
50% 1.907094e-01 0.4070332544 NA NA
99% 8.948397e-02 1.0147742127 NA NA
95% 1.136333e-01 0.6956807816 NA NA
50% 1.930525e-01 0.3513574274 NA NA
99% 2.447548e-02 1.1579926143 NA NA
95% 3.840200e-02 0.6379976750 NA NA
50% 8.638646e-02 0.2127636754 NA NA
99% 6.808129e-04 0.0069026874 NA NA
95% 8.093970e-04 0.0047446615 NA NA
50% 1.359860e-03 0.0024944051 NA NA
99% 1.496488e-01 1.3279197458 NA NA
95% 1.857421e-01 0.9494241676 NA NA
50% 2.941147e-01 0.5120123120 NA NA
99% 1.890780e-05 0.0007127429 NA NA
95% 1.890780e-05 0.0003658254 NA NA
50% 7.672739e-05 0.0001345470 NA NA
99% 1.207518e-02 0.1620087495 NA NA
95% 1.572631e-02 0.1119188217 NA NA
50% 2.634906e-02 0.0516056816 NA NA
99% 2.700307e-01 0.8222671675 NA NA
95% 3.044782e-01 0.7050738787 NA NA
50% 3.942036e-01 0.5243299988 NA NA
99% 1.802251e-01 0.6442898216 NA NA
95% 2.067320e-01 0.5411396284 NA NA
50% 2.773070e-01 0.3849371144 NA NA
99% 2.205680e-01 0.6997578542 NA NA
95% 2.488732e-01 0.5981778381 NA NA
50% 3.220708e-01 0.4359213865 NA NA
99% 3.824667e-02 0.2409330525 NA NA
95% 4.587063e-02 0.1852713188 NA NA
50% 6.742440e-02 0.1095864400 NA NA
99% 2.120332e-02 0.8416074221 0.872685694 0.890497395
95% 3.186792e-02 0.4663583207 NA NA
50% 6.296150e-02 0.1569536579 NA NA
99% 3.486135e-02 0.1482259441 NA NA
95% 4.025985e-02 0.1211132139 NA NA
50% 5.578103e-02 0.0810858259 NA NA
99% 4.822250e-02 0.1597377269 NA NA
95% 5.366698e-02 0.1333028354 NA NA
50% 7.067304e-02 0.0964532077 NA NA
99% 1.745459e-01 0.6014610761 NA NA
95% 1.974005e-01 0.5046382874 NA NA
50% 2.634828e-01 0.3617825420 NA NA
99% 3.724426e-01 8.0885366087 NA NA
95% 5.174663e-01 5.0329580174 NA NA
50% 9.661947e-01 2.0487978404 NA NA
99% 3.168584e-01 2.1439744624 NA NA
[ reached getOption("max.print") -- omitted 152 rows ]Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#object with cleaned-up row namesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentinterval<-rep(c("99%", "95%", "50%"), 134)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#replicated each name three times in sequence to match up with new data frameError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentseab.names<-data.frame(rep(names(SEA.B.credibles), each = 3))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(seab.names)<-"names"Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#seab.names<-separate(seab.names, col = names, into = c('hab.season', 'spp'), extra = "merge")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#combine names with new data frameError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentseab.cred.matrix<-data.frame(seab.names, interval, y)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(seab.cred.matrix)<-c(colnames(seab.names), "interval", "lower", "upper")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write out to a csvError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(seab.cred.matrix, file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/global.sc.SEA.B.credibles.csv")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# do similar to get the modes, taking care to pick up multimodal posteriorError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# distributions if presentError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentSEA.B.modes<-lapply(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument as.data.frame(SEA.B), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument function(x,...){tmp<-hdrcde::hdr(x)$mode},Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument prob = cr.p, all.modes=T)Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#need to do the same as for the credible intervals to get the colnames to beError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#the names not the numbersError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(SEA.B.modes, file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/global.sc.SEA.B.modes.csv")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#read back inError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentSEA.B.modes.matrix<-read.csv(file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/global.sc.SEA.B.modes.csv", header = T, row.names = 1)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#change colnamesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(SEA.B.modes.matrix)<-colnames(global.ML)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#transpose this matrixError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentSEA.B.modes.matrix.t<-t(SEA.B.modes.matrix)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#change the name of what is now a single column to modeError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(SEA.B.modes.matrix.t)<-"mode"Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write out the final csv over the previous mode csvError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(SEA.B.modes.matrix.t, file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/global.sc.SEA.B.modes.csv")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.2 Slough, Habitat, Season Pairwise Comparisons
Use pairwise comparisons function from tRophicPosition package
#turn output into a listError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentSEA.B.list <- as.list(as.data.frame(SEA.B))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#do all pairwise comparisons with function from tRophicPosition packageError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentSEA.B.pairwise <- pairwiseComparisons(SEA.B.list, print = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(SEA.B.pairwise) <- rownames(SEA.B.pairwise)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.3 Niche Visualizations
Format data for plotting
post.plot.data <- SEA.B %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_longer(everything(), names_to = "Slough_Habitat_Season_Species", values_to = "SEA_B") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate("Slough_Habitat_Season_Species", into = c("Slough_Habitat_Season", "Species"), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument sep = "[//.]", extra = "merge") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season, into = c("Slough", "Habitat", "Season"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Habitat %in% c("Marsh", "NP", "Pond"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.3.1 Habitat, Season, Slough, Plots
windowsFonts(Times = windowsFont("Times New Roman"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument(post.seab<-ggplot(post.plot.data, aes(x = Species, y = SEA_B, fill = Habitat))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1, alpha = 0.5)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument facet_wrap(~Slough*Season, scales = "free", ncol = 1)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_fill_viridis_d(aesthetics = "fill", option = "D", end = 0.75)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument coord_cartesian(ylim = c(0, 2.5))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme_bw(base_size = 12)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument xlab(NULL)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ylab(expression("Stomach Content Niche Area - SEA"["b"]))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme(text = element_text(family = "Times", size = 12),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #panel.border = element_blank(), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #panel.grid.major = element_blank(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #panel.grid.minor = element_blank(), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.line = element_line(colour = "black"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.ticks.x = element_line(size = 0.05),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument legend.title = element_blank(), legend.text = element_text(size = 8),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument legend.key.size = unit(0.75, "line"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)
ggsave(filename = "Figures/Stomach Contents/Trophic Niche/Post-Invasion/post_SEAb.tiff", Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument plot = post.seab, dpi = 600, width = 150, height = 225, units = "mm")
post.plot.data.seasonal.comparisons <- post.plot.data %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(#filter for SRS marsh species size classes with seasonal comparisonsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Species %in% c("C.uro1", "E.suc1", "F.chr1", "G.hol1", "G.hol2", "H.for2", "H.let1", "L.goo1", "L.goo2", "L.pun2") &Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Slough == "SRS" & Habitat == "Marsh" |Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #now for SRS near-pond speciesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Species %in% c("G.hol1", "G.hol2", "H.for1", "H.for2") & Slough == "SRS" & Habitat == "NP" |Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #now for SRS pond speciesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Species %in% c("F.chr1", "G.hol1", "G.hol2", "H.for1", "H.for2", "L.goo1", "L.goo2", "L.gul2", "L.gul3", "L.gul4",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument "L.pla1", "L.pun2", "P.lat2") & Slough == "SRS" & Habitat == "Pond" |Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #now do the same for TSLError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Species %in% c("F.chr1", "H.for1", "H.for2", "L.goo1", "L.goo2") &Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Slough == "TSL" & Habitat == "Marsh" |Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #no NP dry season TSL data - skip NP Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Species %in% c("G.hol1", "G.hol2", "H.for2", "L.goo1", "L.goo2", "N.pet1") & Slough == "TSL" & Habitat == "Pond"Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument )Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument(post.seab.season.plot<-ggplot(post.plot.data.seasonal.comparisons %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Season = fct_rev(Season)),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument aes(x = Species, y = SEA_B, fill = Season))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1, alpha = 0.8)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument facet_grid(Slough ~ Habitat, scales = "free")+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_fill_viridis_d(aesthetics = "fill", begin = 0.2, end = 0.88)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument coord_cartesian(ylim = c(0, 1.25))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme_bw(base_size = 12)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument xlab(NULL)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ylab(expression("Stomach Content Niche Area - SEA"["b"]))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme(text = element_text(family = "Times", size = 12),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #panel.border = element_blank(), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #panel.grid.major = element_blank(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #panel.grid.minor = element_blank(), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.line = element_line(colour = "black"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.ticks.x = element_line(size = 0.05),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument legend.title = element_blank(), legend.text = element_text(size = 8),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument legend.key.size = unit(0.75, "line"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentggsave(filename = "Figures/Stomach Contents/Trophic Niche/Post-Invasion/post_SEAb_seasonal_comparisons.tiff", Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument plot = post.seab.season.plot,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument dpi = 600, height = 150, width = 225, units = "mm")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
1.4.3.2 Niche Ellipses
(sc.niche.ellipse.plot <- ggplot(post.consumer.nmds %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(size_class %in% c("G.hol2", "L.goo2"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Habitat != "Sawgrass",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument !(Habitat == "NP" & Slough == "TSL")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Species = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument size_class == "G.hol2" ~ "Eastern Mosquitofish (adults)",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument size_class == "L.goo2" ~ "Bluefin Killifish (adults)"Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument )), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument aes(x = MDS1, y = MDS2, color = Season, fill = Season))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #geom_point()+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument stat_conf_ellipse(geom = "polygon", alpha = 0.8)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument facet_grid(Species ~ Habitat*Slough)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument labs(y = "MDS2", x = "MDS1")+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_fill_viridis_d(end = 0.8)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_color_viridis_d(end = 0.8)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument coord_fixed()+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme_bw()+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme(axis.text.x = element_text(size = 6, angle = 45, hjust = 1),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument panel.spacing.x = unit(0.75, "lines"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentggsave(filename = "Figures/Stomach Contents/Trophic Niche/Post-Invasion/sc_niche_ellipse_plot.tiff", Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument plot = sc.niche.ellipse.plot,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument height = 150, width = 200, units = "mm", dpi = 300)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
1.4.4 Seasonal Pairwise Comparisons
Reformat to have seasonal comparisons within each slough-habitat level
1.4.4.1 SRS Marsh Wet-Dry Comparisons
srs.marsh.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.marsh.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Marsh") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Wet), !is.na(Dry))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.marsh.wet.dry.direct.pairwise <- srs.marsh.wet.dry.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(srs.marsh.wet.dry.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., srs.marsh.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Dry < Wet` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Wet-Dry)/Dry)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write this out to excelError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(srs.marsh.wet.dry.direct.pairwise, file = "srs_marsh_wet_dry_SEAB_prob_diff.csv", row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.4.2 SRS Near Pond Wet-Dry Comparisons
srs.np.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "NP") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Wet), !is.na(Dry))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.np.wet.dry.direct.pairwise <- srs.np.wet.dry.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(srs.np.wet.dry.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., srs.np.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Dry < Wet` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Wet-Dry)/Dry)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write this out to excelError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(srs.np.wet.dry.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/srs_np_wet_dry_SEAB_prob_diff.csv",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.4.3 SRS Pond Wet-Dry Comparisons
srs.pond.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Pond") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Wet), !is.na(Dry))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.pond.wet.dry.direct.pairwise <- srs.pond.wet.dry.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(srs.pond.wet.dry.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., srs.pond.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Dry < Wet` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Wet-Dry)/Dry)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write this out to excelError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(srs.pond.wet.dry.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/Stomach Contentssrs_pond_wet_dry_SEAB_prob_diff.csv",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.4.4 TSL Marsh Wet-Dry Comparisons
tsl.marsh.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.marsh.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Marsh") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Wet), !is.na(Dry))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.marsh.wet.dry.direct.pairwise <- tsl.marsh.wet.dry.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.marsh.wet.dry.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., tsl.marsh.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Dry < Wet` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Wet-Dry)/Dry)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BLUE MITE" ~ "Hydrachnidia",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CORIXI" ~ "Corixidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "EPHEME" ~ "Ephemeroptera",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "ERYSIM" ~ "E. simplicicollis",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "GERRID" ~ "Gerridae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write this out to excelError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(tsl.marsh.wet.dry.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/tsl_marsh_wet_dry_SEAB_prob_diff.csv",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.4.5 TSL Near Pond Wet-Dry Comparisons
All TSL near-pond habitats were dry when sampled during the dry season so wet-dry comparisons could not be made
1.4.4.6 TSL Pond Wet-Dry Comparisons
tsl.pond.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Pond") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Wet), !is.na(Dry))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.pond.wet.dry.direct.pairwise <- tsl.pond.wet.dry.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.pond.wet.dry.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., tsl.pond.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Dry < Wet` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Wet-Dry)/Dry)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write this out to excelError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(tsl.pond.wet.dry.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/tsl_pond_wet_dry_SEAB_prob_diff.csv",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.4.7 Combine Output for Seasonal Comparisons
seasonal.pairiwse <- bind_rows(srs.marsh.wet.dry.direct.pairwise, srs.np.wet.dry.direct.pairwise, srs.pond.wet.dry.direct.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument tsl.marsh.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(seasonal.pairiwse, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/season_pairiwise.csv",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#proportion of statistical changes in wet-dry comparisonsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsum(seasonal.pairiwse$`Dry < Wet`>=0.95 | seasonal.pairiwse$`Dry < Wet`<=0.05)/nrow(seasonal.pairiwse) [1] 0.5Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#mean and sd % change all seasonal changesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentseasonal.pairiwse %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Percent Change` = abs(`Percent Change`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#break it down by habitatError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#marshError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsum(srs.marsh.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument srs.marsh.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument tsl.marsh.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument tsl.marsh.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument nrow(bind_rows(srs.marsh.wet.dry.direct.pairwise, tsl.marsh.wet.dry.direct.pairwise))[1] 0.4666667Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#magnitudeError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentbind_rows(srs.marsh.wet.dry.direct.pairwise, tsl.marsh.wet.dry.direct.pairwise)%>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Percent Change` = abs(`Percent Change`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#NP - no TSL NPError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsum(srs.np.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument srs.np.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument nrow(srs.np.wet.dry.direct.pairwise)[1] 1Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#magnitudeError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentbind_rows(srs.np.wet.dry.direct.pairwise)%>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Percent Change` = abs(`Percent Change`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#PondError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsum(srs.pond.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument srs.pond.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument tsl.pond.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument tsl.pond.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument nrow(bind_rows(srs.pond.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise))[1] 0.4210526Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#magnitudeError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentbind_rows(srs.pond.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise)%>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Percent Change` = abs(`Percent Change`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.4.8 Seasonal Percent Change Plots
Format data for plotting
all.season.direct.pairwise <- bind_rows(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument srs.marsh.wet.dry.direct.pairwise, srs.np.wet.dry.direct.pairwise, srs.pond.wet.dry.direct.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument tsl.marsh.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise #no tsl.np.wet.dry.direct.pairwiseError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "C.uro1" ~ "M.uro1",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument T ~ `Size Class`Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument )) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Wet < Dry` = `Dry < Wet`) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Trophic_Guild = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` %in% c("P.lat2" ) ~ "Detritivore",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` %in% c("M.uro1", "H.let1", "G.hol1", "G.hol2", "E.suc1") ~ "Omnivore",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` %in% c("N.pet1", "L.goo1", "L.goo2", "H.for1", "H.for2", "F.chr1") ~ "Invertivore",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` %in% c("L.pun2", "L.pla1", "L.gul4", "L.gul3", "L.gul2") ~ "Mesopredator"Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Trophic_Score = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Trophic_Guild == "Mesopredator" ~ 4,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Trophic_Guild == "Invertivore" ~ 3,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Trophic_Guild == "Omnivore" ~ 2,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Trophic_Guild == "Detritivore" ~ 1Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = fct_reorder(`Size Class`, Trophic_Score)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Trophic Guild` = Trophic_Guild)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentPercent change for seasonal changes
windowsFonts(Times = windowsFont("Times New Roman"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument(season.percent.change.plot <- ggplot(data = all.season.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument aes(y = `Size Class`, x = `Percent Change`, fill = Habitat, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument color = Habitat, shape = Slough))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument geom_point(aes(size = `Wet < Dry`))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_shape_manual(values = c(22,24))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_fill_viridis_d(end = 0.8)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_color_viridis_d(end = 0.8)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_size_continuous(breaks = c(0.05, 0.25, 0.75, 0.95))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument facet_zoom(xlim = c(-100, 100))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument geom_hline(yintercept = 12.5, linetype = "dashed", color = viridis(n=4, option = "A")[1])+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument geom_hline(yintercept = 6.5, linetype = "dashed", color = viridis(n=4, option = "A")[2])+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument geom_hline(yintercept = 1.5, linetype = "dashed", color = viridis(n=4, option = "A")[3])+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme_bw()+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme(text = element_text(family = "Times"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentggsave("Figures/Stomach Contents/Trophic Niche/Post-Invasion/stomach_contents_season_percent_change_plot.tiff", Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument plot = season.percent.change.plot, dpi = 300,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument height = 250, width = 150, units = "mm")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
Boxplots for percent change per habitat across all species
(season.percent.change.boxplot <- ggplot(data = all.season.direct.pairwise %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Habitat = gsub("NP", "Near Pond", Habitat)), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument aes(y = `Percent Change`, x = Habitat))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument geom_boxplot()+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument facet_zoom(ylim = c(-100, 100))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_x_discrete(limits = c("Pond", "Near Pond", "Marsh"))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme_bw()+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme(text = element_text(family = "Times"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
all.season.direct.pairwise.long <- all.season.direct.pairwise %>%
pivot_longer(cols = c(Wet, Dry), names_to = "Season", values_to = "Niche Area") %>%
mutate(Habitat = gsub("NP", "Near Pond", Habitat))
(season.niche.area.habitat.plot <- ggplot(data = all.season.direct.pairwise.long,
aes(x = Season, y = `Niche Area`))+
geom_boxplot()+
facet_zoom(ylim = c(0, 1))+
facet_wrap(~Habitat)+
scale_x_discrete(limits = c("Wet", "Dry"))
#coord_cartesian(ylim = c(0,1))
)
season.niche.shifts <- all.season.direct.pairwise.long %>%
filter(`Wet < Dry` <= 0.05 | `Wet < Dry` >= 0.95)
season.niche.shifts %>% group_by(Habitat, Season) %>% tally()
(sc.season.niche.area.shifts.habitat.plot <- ggplot(data = season.niche.shifts,
aes(x = Season, y = `Niche Area`))+
geom_boxplot()+
#facet_zoom(ylim = c(0, 1))+
facet_wrap(~Habitat)+
scale_x_discrete(limits = c("Wet", "Dry"))+
coord_cartesian(ylim = c(0,1))+
ylab(label = expression(Niche[SCA]))+
theme_bw()+
theme(text = element_text(family = "Times", size = 8),
axis.text.y = element_text(size = 6))
)
ggsave("Figures/Stomach Contents/Trophic Niche/Post-Invasion/stomach_contents_seasonal_niche_shifts_per_habitat.tiff",
plot = sc.season.niche.area.shifts.habitat.plot,
dpi = 300, height = 150, width = 250, units = "mm")
season.niche.shifts %>% group_by(Habitat, Season, `Trophic Guild`) %>% tally() %>%
arrange(Habitat, `Trophic Guild`)
(sc.season.niche.area.shifts.guild.habitat.plot <- ggplot(data = season.niche.shifts,
aes(x = Season, y = `Niche Area`))+
geom_boxplot()+
#facet_zoom(ylim = c(0, 1))+
facet_wrap(~Habitat*`Trophic Guild`, scales = "free_y")+
scale_x_discrete(limits = c("Wet", "Dry"))+
#coord_cartesian(ylim = c(0,1))+
ylab(label = expression(Niche[SCA]))+
theme_bw()+
theme(text = element_text(family = "Times", size = 8),
axis.text.y = element_text(size = 6))
)
ggsave("Figures/Stomach Contents/Trophic Niche/Post-Invasion/stomach_contents_seasonal_niche_shifts_guild_per_habitat.tiff",
plot = sc.season.niche.area.shifts.guild.habitat.plot,
dpi = 300, height = 150, width = 250, units = "mm")1.4.5 Slough Pairwise Comparisons
1.4.5.1 SRS-TSL Marsh Wet Season Comparisons
slough.marsh.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentslough.marsh.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Season == "Wet", Habitat == "Marsh") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(SRS), !is.na(TSL))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentslough.marsh.wet.direct.pairwise <- slough.marsh.wet.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(slough.marsh.wet.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., slough.marsh.wet.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`TSL < SRS` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((TSL-SRS)/SRS)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write this out to excelError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(slough.marsh.wet.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_marsh_wet_SEAB_prob_diff.csv",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.5.2 SRS-TSL Near Pond Wet Comparisons
slough.np.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentslough.np.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Season == "Wet", Habitat == "NP") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(SRS), !is.na(TSL))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentslough.np.wet.direct.pairwise <- slough.np.wet.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(slough.np.wet.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., slough.np.wet.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`TSL < SRS` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((TSL-SRS)/SRS)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write this out to excelError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(slough.np.wet.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_np_wet_SEAB_prob_diff.xlsx",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.5.3 SRS-TSL Pond Wet Comparisons
slough.pond.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentslough.pond.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Season == "Wet", Habitat == "Pond") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(SRS), !is.na(TSL))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentslough.pond.wet.direct.pairwise <- slough.pond.wet.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(slough.pond.wet.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., slough.pond.wet.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`TSL < SRS` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((TSL-SRS)/SRS)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write this out to excelError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(slough.pond.wet.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_pond_wet_SEAB_prob_diff.csv",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.5.4 SRS-TSL Marsh Dry Season Comparisons
slough.marsh.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentslough.marsh.dry.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Season == "Dry", Habitat == "Marsh") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(SRS), !is.na(TSL))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentslough.marsh.dry.direct.pairwise <- slough.marsh.dry.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(slough.marsh.dry.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., slough.marsh.dry.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`TSL < SRS` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((TSL-SRS)/SRS)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "ERYSIM" ~ "E. simplicicollis",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write this out to excelError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(slough.marsh.dry.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_marsh_dry_SEAB_prob_diff.xlsx",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.5.5 SRS-TSL Near Pond Dry Comparisons
Again, no TSL near-pond dry-season data because these habitats had completely dried by the time of our dry-season sampling
1.4.5.6 SRS-TSL Pond Dry Comparisons
#write this out to excel
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(slough.pond.dry.direct.pairwise, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_pond_dry_SEAB_prob_diff.csv",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.5.7 Combine Output for Slough Comparisons
slough.pairwise <- bind_rows(slough.marsh.dry.direct.pairwise, slough.pond.dry.direct.pairwise,
slough.marsh.wet.direct.pairwise, slough.np.wet.direct.pairwise, slough.pond.wet.direct.pairwise)
write.csv(slough.pairwise,
"Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_pairwise.csv",
row.names = F)
#proportion of statistical changes in slough comparisons
sum(slough.pairwise$`TSL < SRS`>=0.95 | slough.pairwise$`TSL < SRS`<=0.05)/nrow(slough.pairwise)
#mean and sd % change all seasonal changes
slough.pairwise %>%
filter(`TSL < SRS` >= 0.95 | `TSL < SRS` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))1.4.6 Habitat Pairwise Comparisons
1.4.6.1 SRS Marsh-Near Pond Wet Season Comparisons
srs.wet.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.wet.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat %in% c("Marsh", "NP"), Season == "Wet") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(NP))
srs.wet.marsh.np.direct.pairwise <- srs.wet.marsh.np.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.marsh.np.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.wet.marsh.np.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < NP` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < NP` >= 0.95 ~ "Decrease",
`Marsh < NP` < 0.05 ~ "Increase",
`Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
`Percent Change` = ((NP-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)
#write this out to excel
#write.csv(srs.wet.marsh.np.direct.pairwise,
#file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/srs_wet_marsh_np_SEAB_prob_diff.csv",
#row.names = F1.4.6.2 SRS Marsh-Pond Wet Comparisons
srs.wet.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.wet.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat %in% c("Marsh", "Pond"), Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Marsh), !is.na(Pond))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.wet.marsh.pond.direct.pairwise <- srs.wet.marsh.pond.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.marsh.pond.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., srs.wet.marsh.pond.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Marsh < Pond` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Pond-Marsh)/Marsh)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "ERYSIM" ~ "E. simplicicollis",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "LIBINC" ~ "L. incesta",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROSPP" ~ "Procambarus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.6.3 SRS Near Pond-Pond Wet Season Comparisons
srs.wet.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.wet.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat %in% c("NP", "Pond"), Season == "Wet") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(NP), !is.na(Pond))
srs.wet.np.pond.direct.pairwise <- srs.wet.np.pond.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.np.pond.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.wet.np.pond.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>%
rename(`NP < Pond` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`NP < Pond` >= 0.95 ~ "Decrease",
`NP < Pond` < 0.05 ~ "Increase",
`NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-NP)/NP)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "LIBINC" ~ "L. incesta",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)1.4.6.4 SRS Marsh-Near Pond Dry Season Comparisons
srs.dry.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
#unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "SizeClass") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>%
mutate(SizeClass = gsub("^.", "", SizeClass)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
filter(SizeClass %in% colnames(.)) %>%
select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>%
unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_SizeClass")
#format modes to bind with pairwise probs
srs.dry.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>%
rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>%
mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>%
mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>%
filter(Slough == "SRS", Habitat %in% c("Marsh", "NP"), Season == "Dry") %>%
pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>%
filter(!is.na(Marsh), !is.na(NP))
srs.dry.marsh.np.direct.pairwise <- srs.dry.marsh.np.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.marsh.np.pairwise)) %>%
separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>%
filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>%
merge(., srs.dry.marsh.np.modes.wide, by = "Size Class") %>%
select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>%
rename(`Marsh < NP` = ".") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < NP` >= 0.95 ~ "Decrease",
`Marsh < NP` < 0.05 ~ "Increase",
`Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
`Percent Change` = ((NP-Marsh)/Marsh)*100,
`Size Class` = case_when(
`Size Class` == "AMPHIPOD" ~ "Amphipoda",
`Size Class` == "BELSPP" ~ "Belostoma spp.",
`Size Class` == "BRAGRA" ~ "B. gravida",
`Size Class` == "COPEPOD" ~ "Copepoda",
`Size Class` == "CHIRON" ~ "Chironomidae",
`Size Class` == "COENAG" ~ "Coenagrionidae",
`Size Class` == "CELSPP" ~ "Celithimus spp.",
`Size Class` == "ERYSIM" ~ "E. simplicicollis",
`Size Class` == "PALPAL" ~ "P. paludosus",
`Size Class` == "PELFEM" ~ "P. femoratus",
`Size Class` == "PROFAL" ~ "P. fallax",
`Size Class` == "PROALL" ~ "P. alleni",
`Size Class` == "PROSPP" ~ "Procambarus spp.",
TRUE ~ `Size Class`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)1.4.6.5 SRS Marsh-Pond Dry Season Comparisons
srs.dry.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.dry.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat %in% c("Marsh", "Pond"), Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Marsh), !is.na(Pond))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.dry.marsh.pond.direct.pairwise <- srs.dry.marsh.pond.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.marsh.pond.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., srs.dry.marsh.pond.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(
`Marsh < Pond` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Pond-Marsh)/Marsh)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "ERYSIM" ~ "E. simplicicollis",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "LIBINC" ~ "L. incesta",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PLASCA" ~ "Planorbella spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROSPP" ~ "Procambarus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.6.6 SRS Near Pond-Pond Dry Season Comparisons
srs.dry.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.dry.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "SRS", Habitat %in% c("NP", "Pond"), Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(NP), !is.na(Pond))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsrs.dry.np.pond.direct.pairwise <- srs.dry.np.pond.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.np.pond.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., srs.dry.np.pond.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`NP < Pond` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `NP < Pond` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `NP < Pond` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Pond-NP)/NP)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "ERYSIM" ~ "E. simplicicollis",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "LIBINC" ~ "L. incesta",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROSPP" ~ "Procambarus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.6.7 TSL Marsh-Near Pond Wet Season Comparisons
tsl.wet.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.wet.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat %in% c("Marsh", "NP"), Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Marsh), !is.na(NP))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.wet.marsh.np.direct.pairwise <- tsl.wet.marsh.np.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.marsh.np.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., tsl.wet.marsh.np.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Marsh < NP` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < NP` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < NP` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((NP-Marsh)/Marsh)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BLUE MITE" ~ "Hydrachnidia",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COLEOA" ~ "Coleoptera",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "ERYSIM" ~ "E. simplicicollis",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "GERRID" ~ "Gerridae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PLASCA" ~ "Planorbella spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROSPP" ~ "Procambarus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.6.8 TSL Marsh-Pond Wet Season Comparisons
tsl.wet.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.wet.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat %in% c("Marsh", "Pond"), Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Marsh), !is.na(Pond))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.wet.marsh.pond.direct.pairwise <- tsl.wet.marsh.pond.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.marsh.pond.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., tsl.wet.marsh.pond.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Marsh < Pond` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Pond-Marsh)/Marsh)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "ERYSIM" ~ "E. simplicicollis",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "LIBINC" ~ "L. incesta",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROSPP" ~ "Procambarus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.6.9 TSL NP-Pond Wet Season Comparisons
tsl.wet.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.wet.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat %in% c("NP", "Pond"), Season == "Wet") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(NP), !is.na(Pond))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.wet.np.pond.direct.pairwise <- tsl.wet.np.pond.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.np.pond.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., tsl.wet.np.pond.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`NP < Pond` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `NP < Pond` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `NP < Pond` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Pond-NP)/NP)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "ERYSIM" ~ "E. simplicicollis",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "LIBINC" ~ "L. incesta",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROSPP" ~ "Procambarus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.6.10 TSL Marsh-Near Pond Dry Season Comparisons
No dry-season TSL near-pond data
1.4.6.11 TSL Marsh-Pond Dry Season Comparisons
tsl.dry.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(-c(Slough, Habitat, Season)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument t() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(SizeClass = gsub("^.", "", SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(SizeClass %in% colnames(.)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument column_to_rownames(var = "Slough_Habitat_Season_SizeClass")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#format modes to bind with pairwise probsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.dry.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough = gsub("\\[[[0-9]+] ", "", Slough)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(Slough == "TSL", Habitat %in% c("Marsh", "Pond"), Season == "Dry") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!is.na(Marsh), !is.na(Pond))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumenttsl.dry.marsh.pond.direct.pairwise <- tsl.dry.marsh.pond.pairwise %>% as.matrix() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument diag() %>% as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.dry.marsh.pond.pairwise)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument merge(., tsl.dry.marsh.pond.modes.wide, by = "Size Class") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(`Marsh < Pond` = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(#`Pre > Post` = 1 - `Pre < Post`,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Direction = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` >= 0.95 ~ "Decrease",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` < 0.05 ~ "Increase",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Percent Change` = ((Pond-Marsh)/Marsh)*100,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` = case_when(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "AMPHIPOD" ~ "Amphipoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BELSPP" ~ "Belostoma spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "BRAGRA" ~ "B. gravida",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COPEPOD" ~ "Copepoda",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CHIRON" ~ "Chironomidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "COENAG" ~ "Coenagrionidae",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "CELSPP" ~ "Celithimus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "ERYSIM" ~ "E. simplicicollis",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "LIBINC" ~ "L. incesta",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PALPAL" ~ "P. paludosus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PELFEM" ~ "P. femoratus",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PLASCA" ~ "Planorbella spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROFAL" ~ "P. fallax",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROALL" ~ "P. alleni",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument `Size Class` == "PROSPP" ~ "Procambarus spp.",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument TRUE ~ `Size Class`)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(across(where(is.numeric), round, digits = 2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument1.4.6.12 TSL Near Pond-Pond Dry Season Comparisons
No dry-season TSL near-pond data
1.4.6.13 Combine Habitat Comparisons
#Marsh-NP
marsh.np.comparisons <- bind_rows(srs.wet.marsh.np.direct.pairwise, srs.dry.marsh.np.direct.pairwise,
tsl.wet.marsh.np.direct.pairwise)
#proportion of statistical changes in marsh-np comparisons
sum(marsh.np.comparisons$`Marsh < NP`>=0.95 | marsh.np.comparisons$`Marsh < NP`<=0.05)/nrow(marsh.np.comparisons)
#mean and sd % change
marsh.np.comparisons %>%
filter(`Marsh < NP` >= 0.95 | `Marsh < NP` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean,sd = sd))
#Marsh-Pond
marsh.pond.comparisons <- bind_rows(srs.wet.marsh.pond.direct.pairwise, srs.dry.marsh.pond.direct.pairwise,
tsl.wet.marsh.pond.direct.pairwise, tsl.dry.marsh.pond.direct.pairwise)
#proportion of statistical changes in marsh-pond comparisons
sum(marsh.pond.comparisons$`Marsh < Pond`>=0.95 | marsh.pond.comparisons$`Marsh < Pond`<=0.05)/nrow(marsh.pond.comparisons)
#mean and sd % change
marsh.pond.comparisons %>%
filter(`Marsh < Pond` >= 0.95 | `Marsh < Pond` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
#NP-Pond
np.pond.comparisons <- bind_rows(srs.wet.np.pond.direct.pairwise, srs.dry.np.pond.direct.pairwise,
tsl.wet.np.pond.direct.pairwise)
#proportion of statistical changes in marsh-np comparisons
sum(np.pond.comparisons$`NP < Pond`>=0.95 | np.pond.comparisons$`NP < Pond`<=0.05)/nrow(np.pond.comparisons)
#mean and sd % change
np.pond.comparisons %>%
filter(`NP < Pond` >= 0.95 | `NP < Pond` <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
#combine into a list and export to Excel
habitat.comparisons <- list("Marsh-NP" = marsh.np.comparisons, "Marsh-Pond" = marsh.pond.comparisons,
"NP-Pond" = np.pond.comparisons)
write.xlsx(habitat.comparisons, "habitat_comparisons.xlsx")
#proportion of all habitat comparisons that were statistically different
(
sum(marsh.np.comparisons$`Marsh < NP`>=0.95 | marsh.np.comparisons$`Marsh < NP`<=0.05)+
sum(marsh.pond.comparisons$`Marsh < Pond`>=0.95 | marsh.pond.comparisons$`Marsh < Pond`<=0.05)+
sum(np.pond.comparisons$`NP < Pond`>=0.95 | np.pond.comparisons$`NP < Pond`<=0.05)
)/(
nrow(marsh.np.comparisons)+nrow(marsh.pond.comparisons)+nrow(np.pond.comparisons)
)
#mean sd % change for all habitat comparisons
bind_rows(
marsh.np.comparisons %>% rename(Comparison = `Marsh < NP`),
marsh.pond.comparisons %>% rename(Comparison = `Marsh < Pond`),
np.pond.comparisons %>% rename(Comparison = `NP < Pond`)) %>%
filter(Comparison >= 0.95 | Comparison <= 0.05) %>%
mutate(`Percent Change` = abs(`Percent Change`)) %>%
summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))2 Pre- vs Post-Invasion
2.1 Load data
This file is output from 03_diets_from_stomach_contents.Rmd
pre.post.invasion.stomachs <- read.csv("Outputs/Stomach Contents/Diet/Pre-vs-Post-Invasion/pre_post_nmds_consumers.csv") %>%
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument mutate(size_class = gsub("[//.]", "_", size_class))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument2.2 Format data for SIBER
pre.post.siber.data <- pre.post.invasion.stomachs %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(community, c("Habitat", "Season", "Invasion_Status"), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rename(group = size_class, iso1 = MDS1, iso2 = MDS2) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument select(iso1, iso2, group, community) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument group_by(group, community) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(n()>2, sd(iso1)!=0, sd(iso2)!=0, !is.na(iso1), !is.na(iso2)) %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ungroup() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument as.data.frame() %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument arrange(community, group)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#createSiberObjectError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.siber<-createSiberObject(pre.post.siber.data)Warning: At least one of your groups has less than 5 observations.
The absolute minimum sample size for each group is 3 in order
for the various ellipses and corresponding metrics to be
calculated. More reasonably though, a minimum of 5 data points
are required to calculate the two means and the 2x2 covariance
matrix and not run out of degrees of freedom. Check the item
named 'sample.sizes' in the object returned by this function
in order to locate the offending group. Bear in mind that NAs in
the sample.size matrix simply indicate groups that are not
present in that community, and is an acceptable data structure
for these analyses.Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write out samples sizes tableError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(pre.post.siber[["sample.sizes"]] %>% t(), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Diet/Pre-vs-Post-Invasion/siber_pre_post_stomachs_n_table.csv")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentVisualize SIBER object
# Create lists of plotting arguments to be passed onwards to each Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# of the three plotting functions.Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcommunity.hulls.args <- list(col = 1, lty = 1, lwd = 1)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentgroup.ellipses.args <- list(n = 100, p.interval = 0.95, lty = 1, lwd = 2)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentgroup.hull.args <- list(lty = 2, col = "grey20")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpar(mfrow=c(1,1))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentplotSiberObject(pre.post.siber,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ax.pad = 2, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument hulls = F, community.hulls.args, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ellipses = T, group.ellipses.args,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument group.hulls = T, group.hull.args,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument bty = "L",Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument iso.order = c(1,2),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument xlab = expression(paste(delta^{13}, "C (\u2030)")),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ylab = expression(paste(delta^{15}, "N (\u2030)"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
2.3 Calculate TA, SEA, SEAc
pre.post.ML <- groupMetricsML(pre.post.siber)
print(pre.post.ML)
write.csv(t(pre.post.ML), file = "Outputs/Stomach Contents/Diet/Pre-vs-Post-Invasion/pre_post_ML.csv")2.4 SIBER model
#use the same model parametersError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# fit the ellipses which uses an Inverse Wishart priorError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# on the covariance matrix Sigma, and a vague normal prior on the Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# means. Fitting is via the JAGS method.Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.ellipses.posterior <- siberMVN(pre.post.siber, parms, priors)Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
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Compiling model graph
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Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
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Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
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Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
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Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
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Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 33
Unobserved stochastic nodes: 3
Total graph size: 48
Initializing model
|
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 27
Unobserved stochastic nodes: 3
Total graph size: 42
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|******************************** | 64%
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|
|************************************** | 76%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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|******************************** | 64%
|
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|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
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|*************************************** | 78%
|
|**************************************** | 80%
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|***************************************** | 82%
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|****************************************** | 84%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 24
Unobserved stochastic nodes: 3
Total graph size: 39
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|
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|**************** | 32%
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|
|********************* | 42%
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|********************** | 44%
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|*********************** | 46%
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|************************ | 48%
|
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|************************** | 52%
|
|*************************** | 54%
|
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|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
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|******************************************* | 86%
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|******************************************** | 88%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******* | 14%
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|********* | 18%
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|**************** | 32%
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|***************** | 34%
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|
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|********************* | 42%
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|********************** | 44%
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|************************ | 48%
|
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|
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|
|*************************** | 54%
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
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|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
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|****************************************** | 84%
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|******************************************* | 86%
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|******************************************** | 88%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 50
Unobserved stochastic nodes: 3
Total graph size: 65
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
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|
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|
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|
|**************************** | 56%
|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
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|* | 2%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 26
Unobserved stochastic nodes: 3
Total graph size: 41
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|******************************** | 64%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 26
Unobserved stochastic nodes: 3
Total graph size: 41
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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|
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|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
|************************ | 48%
|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
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|
|********************************** | 68%
|
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|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 41
Unobserved stochastic nodes: 3
Total graph size: 56
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
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|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
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|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 38
Unobserved stochastic nodes: 3
Total graph size: 53
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
|
|********* | 18%
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|********** | 20%
|
|*********** | 22%
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|************ | 24%
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|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 57
Unobserved stochastic nodes: 3
Total graph size: 72
Initializing model
|
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|********** | 20%
|
|************ | 24%
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|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 34
Unobserved stochastic nodes: 3
Total graph size: 49
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 31
Unobserved stochastic nodes: 3
Total graph size: 46
Initializing model
|
| | 0%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|* | 2%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 15
Unobserved stochastic nodes: 3
Total graph size: 30
Initializing model
|
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|* | 2%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|******************************** | 64%
|
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|********************************** | 68%
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|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
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|*************************************** | 78%
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|**************************************** | 80%
|
|***************************************** | 82%
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|****************************************** | 84%
|
|******************************************* | 86%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|**************** | 32%
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
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|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
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|******************************************* | 86%
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|******************************************** | 88%
|
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 24
Unobserved stochastic nodes: 3
Total graph size: 39
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|********* | 18%
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|
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|
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|
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|
|***************************** | 58%
|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|****** | 12%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 26
Unobserved stochastic nodes: 3
Total graph size: 41
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
|********************************************* | 90%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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|
|******************************** | 64%
|
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|
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|
|************************************** | 76%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|********* | 18%
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|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|********** | 20%
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|************ | 24%
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|
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|
|**************** | 32%
|
|***************** | 34%
|
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|
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|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 34
Unobserved stochastic nodes: 3
Total graph size: 49
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********* | 18%
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|
|*********** | 22%
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|************ | 24%
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|
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|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 49
Unobserved stochastic nodes: 3
Total graph size: 64
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 27
Unobserved stochastic nodes: 3
Total graph size: 42
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 26
Unobserved stochastic nodes: 3
Total graph size: 41
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 51
Unobserved stochastic nodes: 3
Total graph size: 66
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 35
Unobserved stochastic nodes: 3
Total graph size: 50
Initializing model
|
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|* | 2%
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|
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|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 42
Unobserved stochastic nodes: 3
Total graph size: 57
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
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|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|******************** | 40%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 33
Unobserved stochastic nodes: 3
Total graph size: 48
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 20
Unobserved stochastic nodes: 3
Total graph size: 35
Initializing model
|
| | 0%
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|** | 4%
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|**** | 8%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|************************************* | 74%
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|************************************** | 76%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 27
Unobserved stochastic nodes: 3
Total graph size: 42
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
|
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|
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|**************************************** | 80%
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|
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|*** | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|* | 2%
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|*** | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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|* | 2%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|******************************** | 64%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 11
Unobserved stochastic nodes: 3
Total graph size: 26
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|**************** | 32%
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|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|**************** | 32%
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|****************** | 36%
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|********************** | 44%
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|*********************** | 46%
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|
|******************************* | 62%
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|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|************************************ | 72%
|
|************************************* | 74%
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|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
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|******************************************** | 88%
|
|********************************************* | 90%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|********** | 20%
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|
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|
|**************** | 32%
|
|***************** | 34%
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|****************** | 36%
|
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|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
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|********* | 18%
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|********** | 20%
|
|*********** | 22%
|
|************ | 24%
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 43
Unobserved stochastic nodes: 3
Total graph size: 58
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 46
Unobserved stochastic nodes: 3
Total graph size: 61
Initializing model
|
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|** | 4%
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|**** | 8%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 19
Unobserved stochastic nodes: 3
Total graph size: 34
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|******************************** | 64%
|
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|************************************** | 76%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******* | 14%
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|**************** | 32%
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|********************** | 44%
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|************************ | 48%
|
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|
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|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
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|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
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|****************************************** | 84%
|
|******************************************* | 86%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|**************** | 32%
|
|***************** | 34%
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|
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|********************** | 44%
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|************************ | 48%
|
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|
|****************************** | 60%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
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|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|****************************************** | 84%
|
|******************************************* | 86%
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|******************************************** | 88%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|
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|
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|
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|
|********************** | 44%
|
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|
|************************ | 48%
|
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|
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|
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|**************************** | 56%
|
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|
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|
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|
|******************************** | 64%
|
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|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
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|* | 2%
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|*** | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12
Unobserved stochastic nodes: 3
Total graph size: 27
Initializing model
|
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 88
Unobserved stochastic nodes: 3
Total graph size: 103
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 42
Unobserved stochastic nodes: 3
Total graph size: 57
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|
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|
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|
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|
|******************************** | 64%
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|
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|************************************** | 76%
|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******** | 16%
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|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
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|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
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|***************************************** | 82%
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|****************************************** | 84%
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|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
|**************** | 32%
|
|***************** | 34%
|
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|
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|
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|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|*************************** | 54%
|
|**************************** | 56%
|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
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|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|************** | 28%
|
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|
|**************** | 32%
|
|***************** | 34%
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|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
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|
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|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
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|* | 2%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 21
Unobserved stochastic nodes: 3
Total graph size: 36
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
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|* | 2%
|
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
|
|* | 2%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 20
Unobserved stochastic nodes: 3
Total graph size: 35
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|******************************** | 64%
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|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 19
Unobserved stochastic nodes: 3
Total graph size: 34
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
|
|********************************* | 66%
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|********************************** | 68%
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|*********************************** | 70%
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|************************************ | 72%
|
|************************************* | 74%
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|************************************** | 76%
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|*************************************** | 78%
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|**************************************** | 80%
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|****************************************** | 84%
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|******************************************* | 86%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|**************** | 32%
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|***************** | 34%
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|******************** | 40%
|
|********************* | 42%
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|********************** | 44%
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|************************ | 48%
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|*********************************** | 70%
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|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
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|**************************************** | 80%
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|****************************************** | 84%
|
|******************************************* | 86%
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|******************************************** | 88%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|
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|
|********************* | 42%
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|********************** | 44%
|
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|
|************************ | 48%
|
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|
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|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
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|
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|
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|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
|
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|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|
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|
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|
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|
|******************************* | 62%
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|******************************** | 64%
|
|********************************* | 66%
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|************************************ | 72%
|
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|
|************************************** | 76%
|
|*************************************** | 78%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
|
|* | 2%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 9
Unobserved stochastic nodes: 3
Total graph size: 24
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
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|* | 2%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 19
Unobserved stochastic nodes: 3
Total graph size: 34
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
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|* | 2%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 12
Unobserved stochastic nodes: 3
Total graph size: 27
Initializing model
|
| | 0%
|
|* | 2%
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|*** | 6%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 7
Unobserved stochastic nodes: 3
Total graph size: 22
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|******************************** | 64%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 8
Unobserved stochastic nodes: 3
Total graph size: 23
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
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|*********************************** | 70%
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|************************************ | 72%
|
|************************************* | 74%
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|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
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|****************************************** | 84%
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|******************************************* | 86%
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|******************************************** | 88%
|
|********************************************* | 90%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 16
Unobserved stochastic nodes: 3
Total graph size: 31
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
|************ | 24%
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|
|*************** | 30%
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|**************** | 32%
|
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|
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|
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|********************** | 44%
|
|*********************** | 46%
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|
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|
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|
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|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 5
Unobserved stochastic nodes: 3
Total graph size: 20
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********* | 18%
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|********** | 20%
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|
|*************** | 30%
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|**************** | 32%
|
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|****************** | 36%
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|
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|********************** | 44%
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|*********************** | 46%
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|************************ | 48%
|
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|
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|
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|
|**************************** | 56%
|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|**************** | 32%
|
|***************** | 34%
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|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 25
Unobserved stochastic nodes: 3
Total graph size: 40
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
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|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 26
Unobserved stochastic nodes: 3
Total graph size: 41
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 17
Unobserved stochastic nodes: 3
Total graph size: 32
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 18
Unobserved stochastic nodes: 3
Total graph size: 33
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|******************************** | 64%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 23
Unobserved stochastic nodes: 3
Total graph size: 38
Initializing model
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
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|******************************** | 64%
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|
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|
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|
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|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 29
Unobserved stochastic nodes: 3
Total graph size: 44
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|
|**************** | 32%
|
|***************** | 34%
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|
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|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
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|
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|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
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|****************************************** | 84%
|
|******************************************* | 86%
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|******************************************** | 88%
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|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 14
Unobserved stochastic nodes: 3
Total graph size: 29
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|************ | 24%
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|
|***************** | 34%
|
|****************** | 36%
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|
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|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
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|************************ | 48%
|
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|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
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|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
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|**************************************** | 80%
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|****************************************** | 84%
|
|******************************************* | 86%
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|******************************************** | 88%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 36
Unobserved stochastic nodes: 3
Total graph size: 51
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|
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|
|**************** | 32%
|
|***************** | 34%
|
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|
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|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
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|
|************************ | 48%
|
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|
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|
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|
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|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|***** | 10%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 15
Unobserved stochastic nodes: 3
Total graph size: 30
Initializing model
|
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|* | 2%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 32
Unobserved stochastic nodes: 3
Total graph size: 47
Initializing model
|
| | 0%
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|* | 2%
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|*** | 6%
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|**** | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
| | 0%
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|* | 2%
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|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 10
Unobserved stochastic nodes: 3
Total graph size: 25
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 6
Unobserved stochastic nodes: 3
Total graph size: 21
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 28
Unobserved stochastic nodes: 3
Total graph size: 43
Initializing model
|
| | 0%
|
|* | 2%
|
|*** | 6%
|
|**** | 8%
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 22
Unobserved stochastic nodes: 3
Total graph size: 37
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|***** | 10%
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|****** | 12%
|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 4
Unobserved stochastic nodes: 3
Total graph size: 19
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
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|******************************** | 64%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 3
Unobserved stochastic nodes: 3
Total graph size: 18
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******************************** | 64%
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 13
Unobserved stochastic nodes: 3
Total graph size: 28
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|******* | 14%
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|******** | 16%
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|********* | 18%
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|**************** | 32%
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|***************** | 34%
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|
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|
|******************** | 40%
|
|********************* | 42%
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|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
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|
|************************** | 52%
|
|**************************** | 56%
|
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|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 26
Unobserved stochastic nodes: 3
Total graph size: 41
Initializing model
|
| | 0%
|
|* | 2%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********* | 18%
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|********** | 20%
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|*********** | 22%
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|************ | 24%
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|************** | 28%
|
|*************** | 30%
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|**************** | 32%
|
|***************** | 34%
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|****************** | 36%
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|
|******************** | 40%
|
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|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
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|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
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|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 26
Unobserved stochastic nodes: 3
Total graph size: 41
Initializing model
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|********* | 18%
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|********** | 20%
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|*********** | 22%
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|************ | 24%
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|
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|
|**************** | 32%
|
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|
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|
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|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
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|
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|
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|
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|**************************** | 56%
|
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|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
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|**************************************************| 100%Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#calculate posterior estimates of ellipses for all groupsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.SEA.B<-siberEllipses(pre.post.ellipses.posterior)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(pre.post.SEA.B)<-names(pre.post.ellipses.posterior)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument2.4.1 Extract SIBER Output
#calculate credibility intervalsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcr.p<-c(0.5, 0.95, 0.99) #vector of desired quantilesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#call hdrcde_hdr using lapply()Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.SEA.B.credibles<-lapply(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument as.data.frame(pre.post.SEA.B), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument function(x,...){tmp<-hdrcde::hdr(x)$hdr},Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument prob = cr.p)Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#match up group namesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentnames(pre.post.SEA.B.credibles)<-names(pre.post.ellipses.posterior)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#this carries through the row names from matrices in SEA.B.crediblesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#they don't turn out all that clean though, so adding a column later with the same infoError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.y <- do.call(function(...) {Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument tmp <- plyr::rbind.fill.matrix(...)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames(tmp) <- sapply(pre.post.SEA.B.credibles, function(i) {Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument rownames(i)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument })Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument return(tmp)
pre.post.y 1 2 3 4
99% 0.0050182146 9.471239e-02 0.09745388 0.09888046
95% 0.0065536734 5.921998e-02 NA NA
50% 0.0115772902 2.442719e-02 NA NA
99% 0.0743788736 5.590379e-01 NA NA
95% 0.0908530883 4.125874e-01 NA NA
50% 0.1417706743 2.361491e-01 NA NA
99% 0.0392332900 5.869554e-01 NA NA
95% 0.0520400166 3.881871e-01 NA NA
50% 0.0919685640 1.784481e-01 NA NA
99% 0.1078397224 2.467092e+00 NA NA
95% 0.1484628362 1.483155e+00 NA NA
50% 0.2822565292 6.121437e-01 NA NA
99% 0.1036289886 6.744554e-01 0.68732666 0.69510871
95% 0.1254954834 5.100369e-01 NA NA
50% 0.1884507165 3.028980e-01 NA NA
99% 0.1559911801 1.502889e+00 NA NA
95% 0.2037433906 1.072739e+00 NA NA
50% 0.3378984848 5.875927e-01 NA NA
99% 0.0426838573 6.284306e-01 0.65575713 0.66500440
95% 0.0554792781 4.164568e-01 NA NA
50% 0.0982688008 1.923962e-01 NA NA
99% 0.0279249297 3.796792e-01 NA NA
95% 0.0347445838 2.461531e-01 NA NA
50% 0.0587456079 1.141800e-01 NA NA
99% 0.3465133164 8.787306e-01 NA NA
95% 0.3875353546 7.795386e-01 NA NA
50% 0.4829866702 6.132036e-01 NA NA
99% 0.0287229863 1.146089e+00 NA NA
95% 0.0433586252 6.813770e-01 NA NA
50% 0.0935400067 2.303626e-01 NA NA
99% 0.0161922614 5.734197e-02 NA NA
95% 0.0182865940 4.782400e-02 NA NA
50% 0.0243134898 3.374044e-02 NA NA
99% 0.0666109209 1.948293e-01 NA NA
95% 0.0747442663 1.682349e-01 NA NA
50% 0.0968462197 1.276071e-01 NA NA
99% 0.0437362146 2.256830e+00 NA NA
95% 0.0726783008 1.219937e+00 NA NA
50% 0.1686567446 4.122380e-01 NA NA
99% 0.1124663824 1.012502e+00 NA NA
95% 0.1408988715 7.228503e-01 NA NA
50% 0.2255226551 3.920211e-01 NA NA
99% 0.0672187126 7.305235e-01 NA NA
95% 0.0861528093 5.029100e-01 NA NA
50% 0.1410803255 2.553345e-01 NA NA
99% 0.0125685804 6.059258e-01 NA NA
95% 0.0197450207 3.147534e-01 NA NA
50% 0.0419385431 1.046132e-01 NA NA
99% 0.3446036583 9.586088e-01 NA NA
95% 0.3850537871 8.352308e-01 NA NA
50% 0.4893998239 6.368640e-01 NA NA
99% 0.0304731863 4.219159e-01 0.44706759 0.45390151
95% 0.0398504114 2.870970e-01 NA NA
50% 0.0674153512 1.326385e-01 NA NA
99% 0.0340690262 1.014455e-01 NA NA
95% 0.0384392952 8.728793e-02 NA NA
50% 0.0492777225 6.521872e-02 NA NA
99% 0.0241379417 7.489895e-02 NA NA
95% 0.0269160605 6.367125e-02 NA NA
50% 0.0350824940 4.701379e-02 NA NA
99% 0.2793933509 5.894287e-01 NA NA
95% 0.3035321411 5.350863e-01 NA NA
50% 0.3635956495 4.399883e-01 NA NA
99% 0.4869867569 1.771321e+00 NA NA
95% 0.5656820769 1.475516e+00 NA NA
50% 0.7651187088 1.058821e+00 NA NA
99% 0.1754836454 6.569310e-01 NA NA
95% 0.2011886951 5.447194e-01 NA NA
50% 0.2721685618 3.815795e-01 NA NA
99% 0.2825717974 7.959179e-01 NA NA
95% 0.3147187366 6.930714e-01 NA NA
50% 0.4019398603 5.273237e-01 NA NA
99% 0.0521190902 1.615017e-01 NA NA
95% 0.0594875310 1.397005e-01 NA NA
50% 0.0783237448 1.041993e-01 NA NA
99% 0.1344398331 3.902946e-01 NA NA
95% 0.1505169945 3.365610e-01 NA NA
50% 0.1934911912 2.549881e-01 NA NA
99% 0.0288654420 8.412101e-02 NA NA
95% 0.0322251350 7.259133e-02 NA NA
50% 0.0414345791 5.454475e-02 NA NA
99% 0.0406703672 1.239360e-01 NA NA
95% 0.0454880554 1.056916e-01 NA NA
50% 0.0587676673 7.834070e-02 NA NA
99% 0.0222160589 3.809790e-01 NA NA
95% 0.0319179836 2.483489e-01 NA NA
50% 0.0590535004 1.154976e-01 NA NA
99% 0.0906543984 2.569640e-01 NA NA
95% 0.1010566771 2.218481e-01 NA NA
50% 0.1290125346 1.686062e-01 NA NA
99% 0.0219009334 5.043351e-02 NA NA
95% 0.0242043905 4.513743e-02 NA NA
50% 0.0295283528 3.646944e-02 NA NA
99% 0.0308435680 7.222531e-02 NA NA
95% 0.0338768429 6.457254e-02 NA NA
50% 0.0413367118 5.164186e-02 NA NA
99% 0.0209199447 4.191335e-02 NA NA
95% 0.0225677500 3.846040e-02 NA NA
50% 0.0267042702 3.198620e-02 NA NA
99% 0.1439484722 3.585128e-01 NA NA
95% 0.1590159768 3.164332e-01 NA NA
50% 0.1970022313 2.493964e-01 NA NA
99% 0.3696291279 9.762187e-01 NA NA
95% 0.4174771122 8.597474e-01 NA NA
50% 0.5245472598 6.715579e-01 NA NA
99% 0.2013945640 1.566972e+00 NA NA
95% 0.2583384488 1.162164e+00 NA NA
50% 0.4072115035 6.752812e-01 NA NA
99% 0.0277891303 1.625757e-01 NA NA
95% 0.0338184939 1.264343e-01 NA NA
50% 0.0485203977 7.626215e-02 NA NA
99% 0.2164092157 1.578471e+00 NA NA
95% 0.2609196891 1.172040e+00 NA NA
50% 0.4065995853 6.719786e-01 NA NA
99% 0.0990067844 4.460420e+00 NA NA
95% 0.1492381335 2.345576e+00 NA NA
50% 0.3125712503 7.822469e-01 NA NA
99% 0.0047063191 1.852427e-02 NA NA
95% 0.0052232746 1.524505e-02 NA NA
50% 0.0071939738 1.035416e-02 NA NA
99% 0.0476040457 1.034289e+00 NA NA
95% 0.0649108168 6.500427e-01 NA NA
50% 0.1226836331 2.591307e-01 NA NA
99% 0.0120415294 5.458921e-01 NA NA
95% 0.0181257730 2.819930e-01 NA NA
50% 0.0383062603 9.492129e-02 NA NA
99% 0.5295380688 2.339772e+00 NA NA
95% 0.6197423859 1.883285e+00 NA NA
50% 0.8712088074 1.268925e+00 NA NA
99% 0.2055423714 7.367213e-01 NA NA
95% 0.2358008873 6.202529e-01 NA NA
50% 0.3182327173 4.396719e-01 NA NA
99% 0.5888124966 2.163803e+00 NA NA
95% 0.6817366354 1.810046e+00 NA NA
50% 0.9266293463 1.287964e+00 NA NA
99% 0.1057242908 9.878368e-01 NA NA
95% 0.1496180880 7.213018e-01 NA NA
50% 0.2527193138 4.178806e-01 NA NA
99% 0.0515423854 7.171198e-01 NA NA
95% 0.0664506910 4.775525e-01 NA NA
50% 0.1145904728 2.236372e-01 NA NA
99% 0.1592095005 4.761843e-01 NA NA
95% 0.1794582521 4.111172e-01 NA NA
50% 0.2314478366 3.068835e-01 NA NA
99% 0.0031507063 1.494897e-01 NA NA
95% 0.0051661383 8.285313e-02 NA NA
50% 0.0111259312 2.768533e-02 NA NA
99% 0.0748017890 2.918279e-01 NA NA
95% 0.0870101363 2.415440e-01 NA NA
50% 0.1191214162 1.684173e-01 NA NA
99% 0.1892897255 7.292154e-01 NA NA
95% 0.2174047922 6.047577e-01 NA NA
50% 0.2970990364 4.214472e-01 NA NA
99% 0.3884567587 1.449737e+00 NA NA
95% 0.4455438904 1.211532e+00 NA NA
50% 0.6074256976 8.501477e-01 NA NA
99% 0.0013126992 4.424075e-02 0.05030563 0.05056363
95% 0.0017335803 2.440845e-02 NA NA
50% 0.0034134592 8.382433e-03 NA NA
99% 0.0006025928 2.198550e-02 NA NA
95% 0.0008422960 1.258876e-02 NA NA
50% 0.0017241152 4.198147e-03 NA NA
99% 0.0528442984 2.321007e+00 NA NA
95% 0.0790512751 1.232752e+00 NA NA
50% 0.1648384385 4.108972e-01 NA NA
99% 0.0432836463 2.481943e-01 NA NA
95% 0.0506962292 1.911880e-01 NA NA
50% 0.0739526872 1.157086e-01 NA NA
99% 0.2755673037 7.815900e-01 NA NA
95% 0.3066362359 6.754516e-01 NA NA
50% 0.3918114320 5.123952e-01 NA NA
99% 0.0478539733 1.020957e+00 NA NA
95% 0.0662197323 6.240399e-01 NA NA
50% 0.1190043904 2.552290e-01 NA NA
99% 0.1836649173 5.418387e-01 NA NA
95% 0.2073237781 4.634846e-01 NA NA
50% 0.2657577388 3.504069e-01 NA NA
99% 0.0334328912 1.045028e-01 NA NA
95% 0.0374613583 8.943570e-02 NA NA
50% 0.0490987633 6.601364e-02 NA NA
99% 0.0264094658 6.600843e-02 NA NA
95% 0.0293608085 5.813320e-02 NA NA
50% 0.0364050750 4.595218e-02 NA NA
99% 0.3083013129 6.523677e-01 NA NA
95% 0.3349991317 5.933240e-01 NA NA
50% 0.4021442594 4.890161e-01 NA NA
99% 0.2433806134 7.591141e-01 NA NA
95% 0.2791264432 6.546326e-01 NA NA
50% 0.3660300378 4.875564e-01 NA NA
99% 0.0376592111 1.047044e-01 NA NA
95% 0.0423466340 9.156630e-02 NA NA
50% 0.0535564987 6.979192e-02 NA NA
99% 0.0782218081 2.301516e-01 NA NA
95% 0.0878216549 1.977850e-01 NA NA
50% 0.1133940921 1.490357e-01 NA NA
99% 0.2454552623 7.112555e-01 NA NA
95% 0.2768207167 6.139446e-01 NA NA
50% 0.3566053383 4.673497e-01 NA NA
99% 0.0175888167 5.068513e-02 NA NA
95% 0.0195791819 4.378834e-02 NA NA
50% 0.0249099228 3.283587e-02 NA NA
99% 0.0235997650 6.851040e-02 NA NA
95% 0.0265313320 5.942872e-02 NA NA
50% 0.0337254402 4.437663e-02 NA NA
99% 0.0479000093 1.378039e-01 NA NA
95% 0.0534627763 1.197530e-01 NA NA
50% 0.0683197601 8.996699e-02 NA NA
99% 0.0350462704 1.021142e-01 NA NA
95% 0.0391710023 8.823846e-02 NA NA
50% 0.0502832989 6.613823e-02 NA NA
99% 0.0252800809 5.278146e-02 NA NA
95% 0.0274179327 4.785394e-02 NA NA
50% 0.0325589861 3.937686e-02 NA NA
99% 0.0102086848 2.497606e-02 NA NA
95% 0.0113470367 2.233192e-02 NA NA
50% 0.0139051723 1.757764e-02 NA NA
99% 0.0071491419 2.815511e-01 NA NA
95% 0.0100307299 1.439708e-01 NA NA
50% 0.0200677759 4.950460e-02 NA NA
99% 0.0102868320 2.330070e-02 NA NA
95% 0.0112585007 2.087971e-02 NA NA
50% 0.0136928598 1.687734e-02 NA NA
99% 0.1029271594 6.210930e-01 NA NA
95% 0.1247693936 4.774317e-01 NA NA
50% 0.1885177497 2.945229e-01 NA NA
99% 0.1267112890 3.197148e-01 NA NA
95% 0.1402908494 2.826197e-01 NA NA
50% 0.1748392211 2.217688e-01 NA NA
99% 0.2112751432 7.500410e-01 NA NA
95% 0.2431374942 6.345462e-01 NA NA
50% 0.3255379345 4.519189e-01 NA NA
99% 0.0820556880 2.745559e-01 NA NA
95% 0.0933720608 2.323876e-01 NA NA
50% 0.1234381127 1.681509e-01 NA NA
99% 0.0049524243 2.554791e-02 NA NA
95% 0.0058185079 2.015070e-02 NA NA
50% 0.0082991890 1.268501e-02 NA NA
99% 0.0062634185 3.056778e-01 NA NA
95% 0.0104776689 1.700086e-01 NA NA
50% 0.0231054654 5.734922e-02 NA NA
99% 0.0024429142 9.349860e-02 NA NA
95% 0.0033099897 5.126403e-02 NA NA
50% 0.0068481758 1.678056e-02 NA NA
99% 0.0429916227 1.048131e+00 NA NA
95% 0.0638500800 6.404845e-01 NA NA
50% 0.1245372887 2.669126e-01 NA NA
99% 0.0315204839 3.309688e-01 NA NA
95% 0.0397955516 2.299601e-01 NA NA
50% 0.0641598596 1.164669e-01 NA NA
99% 0.0404192438 2.933965e-01 NA NA
[ reached getOption("max.print") -- omitted 338 rows ]Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#object with cleaned-up row namesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.interval<-rep(c("99%", "95%", "50%"), 196)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#replicated each name three times in sequence to match up with new data frameError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.seab.names<-data.frame(rep(names(pre.post.SEA.B.credibles), each = 3))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(pre.post.seab.names)<-"names"Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#seab.names<-separate(seab.names, col = names, into = c('hab.season', 'spp'), extra = "merge")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#combine names with new data frameError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.seab.cred.matrix<-data.frame(pre.post.seab.names, pre.post.interval, pre.post.y)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(pre.post.seab.cred.matrix)<-c(colnames(pre.post.seab.names), "interval", "lower", "upper")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write out to a csvError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(pre.post.seab.cred.matrix, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_credibles.csv", Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument row.names = F)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# do similar to get the modes, taking care to pick up multimodal posteriorError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument# distributions if presentError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.SEA.B.modes<-lapply(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument as.data.frame(pre.post.SEA.B), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument function(x,...){tmp<-hdrcde::hdr(x)$mode},Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument prob = cr.p, all.modes=T)Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Warning: Binning grid too coarse for current (small) bandwidth: consider increasing 'gridsize'Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#need to do the same as for the credible intervals to get the colnames to beError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#the names not the numbersError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(pre.post.SEA.B.modes, file = "Outputs/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#read back inError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.SEA.B.modes.matrix<-read.csv(file = "Outputs/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv", header = T, row.names = 1)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#change colnamesError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(pre.post.SEA.B.modes.matrix)<-names(pre.post.ellipses.posterior)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#transpose this matrixError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.post.SEA.B.modes.matrix.t<-t(pre.post.SEA.B.modes.matrix) %>% as.data.frame()Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#change the name of what is now a single column to modeError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentcolnames(pre.post.SEA.B.modes.matrix.t)<-"Mode"Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#write out the final csv over the previous mode csvError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwrite.csv(pre.post.SEA.B.modes.matrix.t, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument file = "Outputs/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument2.4.2 Test Convergence
#test convergence for all ellipses
pre.post.all.files <- dir(parms$save.dir, full.names = T)
#find which onces are jags model files
pre.post.model.files <- pre.post.all.files[grep("jags_output", pre.post.all.files)]
#test convergence
do.this <- 1 #this number indicates which file, in order, is being looked at
load(pre.post.model.files[do.this])
gelman.diag(output, multivariate = F)
gelman.plot(output, auto.layout = F)
2.4.3 Pre- vs Post-Invasion Pairwise Comparisons
#turn the output into a list
pre.post.SEA.B.list <- as.list(as.data.frame(pre.post.SEA.B))
#use the pairwiseComparisons function from tRophicPosition package
pre.post.pairwise <- pairwiseComparisons(pre.post.SEA.B.list, print = F)
colnames(pre.post.pairwise) <- rownames(pre.post.pairwise)
#reformat data to have only pre-invasion and post-invasion comparstomachsns
pre.post.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
arrange(Species) %>%
unite("Habitat_Season_Spp", c(Habitat, Season, Species), sep = "_") %>%
column_to_rownames(var = "Habitat_Season_Spp") %>%
select(-c(Invasion_Status)) %>%
t() %>% as.data.frame() %>% #Pre-Invasion is now the columns
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
arrange(Species) %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
unite("Habitat_Season_Spp", c(Habitat, Season, Species), sep = "_") %>%
filter(Habitat_Season_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Habitat_Season_Spp") %>%
t() %>% as.data.frame() %>% #Now Post-Invasion is the columns
rownames_to_column(var = "Habitat_Season_Spp") %>%
filter(Habitat_Season_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Habitat_Season_Spp")
#At the end Post-Invasion is the columns and Pre-Invasion is the rows
#The diagonal has Pre vs Post-Invasion comparisons within a habitat-season for a given size class
#format modes to bind with pairwise probs
pre.post.modes.wide <- pre.post.SEA.B.modes.matrix.t %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
pivot_wider(id_cols = Species, names_from = Invasion_Status, values_from = Mode)
pre.post.direct.spp.pairwise <- pre.post.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(pre.post.direct.pairwise)) %>%
separate(Species, into = c("Habitat", "Season", "Genus", "Species"), sep = "_") %>%
unite("Species", c(Genus, Species), sep = "_") %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, contains("Invasion"), everything()) %>% select(-c(Habitat, Season)) %>%
rename(`Pre < Post` = ".") %>%
distinct(Species, .keep_all = T) %>%
separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>%
separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>%
pivot_wider(names_from = "Invasion_Status", values_from = "Mode") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Pre < Post` >= 0.95 ~ "Increase",
`Pre < Post` < 0.05 ~ "Decrease",
`Pre < Post` > 0.05 | `Pre < Post` < 0.95 ~ "No Change"),
`Percent Change` = ((Post-Pre)/Pre)*100,
Species = case_when(
Species == "A_nat" ~ "A. natalis",
Species == "C_uro" ~ "M. urophthalmus",
Species == "E_eve" ~ "E. evergladei",
Species == "E_glo" ~ "E. gloriosus",
Species == "F_chr" ~ "F. chrysotus",
Species == "G_hol" ~ "G. holbrooki",
Species == "H_for" ~ "H. formosa",
Species == "J_flo" ~ "J. floridae",
Species == "L_goo" ~ "L. goodei",
Species == "L_gul" ~ "L. gulosus",
Species == "L_mac" ~ "L. macrochirus",
Species == "L_mar" ~ "L. marginatus",
Species == "L_mic" ~ "L. microlophus",
Species == "L_pun" ~ "L. punctatus",
Species == "L_pla" ~ "L. platyrhincus",
Species == "M_sal" ~ "M. salmoides",
Species == "P_lat" ~ "P. latipinna",
Species == "T_mar" ~ "P. mariae")) %>%
filter(!is.na(`Percent Change`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
arrange(Season, Habitat, Species) %>%
rename("Pre-Invasion" = Pre, "Post-Invasion" = Post) %>%
select(Season, Habitat, Species, `Size Class`, `Pre < Post`, `Pre-Invasion`, `Post-Invasion`,
Direction, `Percent Change`)
#write this out to excel
write.csv(pre.post.direct.spp.pairwise,
file = "Outputs/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_prob_diff.csv",
row.names = F)
#proportion of statistical changes in pre-post comparstomachsns
sum(pre.post.direct.spp.pairwise$`Pre < Post`>=0.95 | pre.post.direct.spp.pairwise$`Pre < Post`<=0.05)/nrow(pre.post.direct.spp.pairwise)
#proportion of changes in the dry season
nrow(
filter(pre.post.direct.spp.pairwise,
`Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Dry"))/
nrow(
filter(pre.post.direct.spp.pairwise, Season == "Dry")
)
#dry season ponds
nrow(
filter(pre.post.direct.spp.pairwise,
`Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Dry", Habitat == "Pond"))/
nrow(
filter(pre.post.direct.spp.pairwise, Season == "Dry", Habitat == "Pond")
)
#dry season marsh
nrow(
filter(pre.post.direct.spp.pairwise,
`Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Dry", Habitat == "Marsh"))/
nrow(
filter(pre.post.direct.spp.pairwise, Season == "Dry", Habitat == "Marsh")
)
#proportion of changes in the Wet season
nrow(
filter(pre.post.direct.spp.pairwise,
`Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Wet"))/
nrow(
filter(pre.post.direct.spp.pairwise, Season == "Wet")
)
#Wet season ponds
nrow(
filter(pre.post.direct.spp.pairwise,
`Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Wet", Habitat == "Pond"))/
nrow(
filter(pre.post.direct.spp.pairwise, Season == "Wet", Habitat == "Pond")
)
#Wet season marsh
nrow(
filter(pre.post.direct.spp.pairwise,
`Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Wet", Habitat == "Marsh"))/
nrow(
filter(pre.post.direct.spp.pairwise, Season == "Wet", Habitat == "Marsh")
)2.4.4 Pre-Invasion Habitat and Season Comparisons
2.4.4.1 Pre-Invasion Marsh vs Pond Wet Season Comparisons
#reformat data to have only pre-invasion wet-season pond-marsh comparisons
#diagonal will be comparisons within a size class
pre.marsh.pond.wet.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Marsh", Season == "Wet") %>%
arrange(Species) %>%
select(-c(Habitat)) %>%
unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
arrange(Species) %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Pond", Season == "Wet") %>%
select(-c(Habitat)) %>%
unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>%
filter(Invasion_Season_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Season_Spp") %>%
filter(Invasion_Season_Spp %in% colnames(.)) %>%
mutate(Habitat = "Pond") %>%
unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>%
column_to_rownames(var = "Habitat_Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
mutate(Habitat = "Marsh") %>%
rownames_to_column(var = "Invasion_Season_Spp") %>%
unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>%
column_to_rownames(var = "Habitat_Invasion_Season_Spp")
#isolate comparisons within a spp size class
#reformat data
#calculate % change
pre.marsh.pond.wet.direct.spp.pairwise <- pre.marsh.pond.wet.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(pre.marsh.pond.wet.direct.pairwise)) %>%
separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>%
unite("Species", c(Genus, Species), sep = "_") %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, ".", `Marsh_Wet_Pre-Invasion`, `Pond_Wet_Pre-Invasion`) %>%
rename(`Marsh < Pond` = ".") %>%
distinct(Species, .keep_all = T) %>%
separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>%
separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>%
pivot_wider(names_from = "Habitat", values_from = "Mode") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < Pond` >= 0.95 ~ "Increase",
`Marsh < Pond` < 0.05 ~ "Decrease",
`Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-Marsh)/Marsh)*100,
Species = case_when(
Species == "A_nat" ~ "A. natalis",
#Species == "C_uro" ~ "M. urophthalmus",
Species == "E_eve" ~ "E. evergladei",
#Species == "E_glo" ~ "E. gloriosus",
Species == "F_chr" ~ "F. chrysotus",
Species == "F_con" ~ "F. confluentus",
Species == "G_hol" ~ "G. holbrooki",
Species == "H_for" ~ "H. formosa",
Species == "J_flo" ~ "J. floridae",
Species == "L_goo" ~ "L. goodei",
Species == "L_gul" ~ "L. gulosus",
#Species == "L_mac" ~ "L. macrochirus",
Species == "L_mar" ~ "L. marginatus",
#Species == "L_mic" ~ "L. microlophus",
Species == "L_pun" ~ "L. punctatus",
#Species == "L_pla" ~ "L. platyrhincus",
#Species == "M_sal" ~ "M. salmoides",
Species == "N_gyr" ~ "N. gyrinus"
#Species == "P_lat" ~ "P. latipinna",
#Species == "T_mar" ~ "P. mariae"
)) %>%
filter(!is.na(`Percent Change`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Invasion_Status, Season, Species, `Size Class`, Marsh, Pond, `Marsh < Pond`,
Direction, `Percent Change`)2.4.4.2 Pre-Invasion Marsh vs Pond Dry Season Comparisons
#reformat data to have only pre-invasion Dry-season pond-marsh comparisons
#diagonal will be comparisons within a size class
pre.marsh.pond.Dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Marsh", Season == "Dry") %>%
arrange(Species) %>%
select(-c(Habitat)) %>%
unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
arrange(Species) %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Pond", Season == "Dry") %>%
select(-c(Habitat)) %>%
unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>%
filter(Invasion_Season_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Season_Spp") %>%
filter(Invasion_Season_Spp %in% colnames(.)) %>%
mutate(Habitat = "Pond") %>%
unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>%
column_to_rownames(var = "Habitat_Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
mutate(Habitat = "Marsh") %>%
rownames_to_column(var = "Invasion_Season_Spp") %>%
unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>%
column_to_rownames(var = "Habitat_Invasion_Season_Spp")
#isolate comparisons within a spp size class
#reformat data
#calculate % change
pre.marsh.pond.Dry.direct.spp.pairwise <- pre.marsh.pond.Dry.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(pre.marsh.pond.Dry.direct.pairwise)) %>%
separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>%
unite("Species", c(Genus, Species), sep = "_") %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, ".", `Marsh_Dry_Pre-Invasion`, `Pond_Dry_Pre-Invasion`) %>%
rename(`Marsh < Pond` = ".") %>%
distinct(Species, .keep_all = T) %>%
separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>%
separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>%
pivot_wider(names_from = "Habitat", values_from = "Mode") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Marsh < Pond` >= 0.95 ~ "Increase",
`Marsh < Pond` < 0.05 ~ "Decrease",
`Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-Marsh)/Marsh)*100,
Species = case_when(
Species == "A_nat" ~ "A. natalis",
#Species == "C_uro" ~ "M. urophthalmus",
Species == "E_eve" ~ "E. evergladei",
#Species == "E_glo" ~ "E. gloriosus",
Species == "F_chr" ~ "F. chrysotus",
#Species == "F_con" ~ "F. confluentus",
Species == "G_hol" ~ "G. holbrooki",
Species == "H_for" ~ "H. formosa",
#Species == "J_flo" ~ "J. floridae",
#Species == "L_goo" ~ "L. goodei",
# Species == "L_gul" ~ "L. gulosus",
#Species == "L_mac" ~ "L. macrochirus",
Species == "L_mar" ~ "L. marginatus",
#Species == "L_mic" ~ "L. microlophus",
Species == "L_pun" ~ "L. punctatus",
#Species == "L_pla" ~ "L. platyrhincus",
#Species == "M_sal" ~ "M. salmoides",
Species == "N_gyr" ~ "N. gyrinus",
Species == "P_lat" ~ "P. latipinna"
#Species == "T_mar" ~ "P. mariae"
)) %>%
filter(!is.na(`Percent Change`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Invasion_Status, Season, Species, `Size Class`, Marsh, Pond, `Marsh < Pond`,
Direction, `Percent Change`)2.4.4.3 Pre-Invasion Wet vs Dry Marsh Comparisons
#reformat data to have only pre-invasion wet-season pond-marsh comparisons
#diagonal will be comparisons within a size class
pre.marsh.wet.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Marsh", Season == "Wet") %>%
arrange(Species) %>%
select(-c(Season)) %>%
unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
arrange(Species) %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Marsh", Season == "Dry") %>%
select(-c(Season)) %>%
unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>%
filter(Invasion_Habitat_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Habitat_Spp") %>%
filter(Invasion_Habitat_Spp %in% colnames(.)) %>%
mutate(Season = "Dry") %>%
unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>%
column_to_rownames(var = "Season_Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
mutate(Season = "Wet") %>%
rownames_to_column(var = "Invasion_Habitat_Spp") %>%
unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>%
column_to_rownames(var = "Season_Invasion_Habitat_Spp")
#isolate comparisons within a spp size class
#reformat data
#calculate % change
pre.marsh.wet.dry.direct.spp.pairwise <- pre.marsh.wet.dry.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(pre.marsh.wet.dry.direct.pairwise)) %>%
separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>%
unite("Species", c(Genus, Species), sep = "_") %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, ".", `Marsh_Wet_Pre-Invasion`, `Marsh_Dry_Pre-Invasion`) %>%
rename(`Wet < Dry` = ".") %>%
distinct(Species, .keep_all = T) %>%
separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>%
separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>%
pivot_wider(names_from = "Season", values_from = "Mode") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Wet < Dry` >= 0.95 ~ "Increase",
`Wet < Dry` < 0.05 ~ "Decrease",
`Wet < Dry` > 0.05 | `Wet < Dry` < 0.95 ~ "No Change"),
`Percent Change` = ((Dry-Wet)/Wet)*100,
Species = case_when(
Species == "A_nat" ~ "A. natalis",
Species == "C_var" ~ "C. variegatus",
#Species == "C_uro" ~ "M. urophthalmus",
Species == "E_eve" ~ "E. evergladei",
Species == "E_glo" ~ "E. gloriosus",
Species == "F_chr" ~ "F. chrysotus",
Species == "F_con" ~ "F. confluentus",
Species == "G_hol" ~ "G. holbrooki",
Species == "H_for" ~ "H. formosa",
Species == "J_flo" ~ "J. floridae",
Species == "L_goo" ~ "L. goodei",
#Species == "L_gul" ~ "L. gulosus",
#Species == "L_mac" ~ "L. macrochirus",
Species == "L_mar" ~ "L. marginatus",
#Species == "L_mic" ~ "L. microlophus",
Species == "L_pun" ~ "L. punctatus",
#Species == "L_pla" ~ "L. platyrhincus",
#Species == "M_sal" ~ "M. salmoides",
Species == "N_gyr" ~ "N. gyrinus",
Species == "P_lat" ~ "P. latipinna"
#Species == "T_mar" ~ "P. mariae"
)) %>%
filter(!is.na(`Percent Change`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Invasion_Status, Habitat, Species, `Size Class`, Wet, Dry, `Wet < Dry`,
Direction, `Percent Change`)2.4.4.4 Pre-Invasion Wet vs Dry Pond Comparisons
#reformat data to have only pre-invasion wet-season pond-Pond comparisons
#diagonal will be comparisons within a size class
pre.pond.wet.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Pond", Season == "Wet") %>%
arrange(Species) %>%
select(-c(Season)) %>%
unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Pre", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
arrange(Species) %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Pond", Season == "Dry") %>%
select(-c(Season)) %>%
unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>%
filter(Invasion_Habitat_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Habitat_Spp") %>%
filter(Invasion_Habitat_Spp %in% colnames(.)) %>%
mutate(Season = "Dry") %>%
unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>%
column_to_rownames(var = "Season_Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
mutate(Season = "Wet") %>%
rownames_to_column(var = "Invasion_Habitat_Spp") %>%
unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>%
column_to_rownames(var = "Season_Invasion_Habitat_Spp")
#isolate comparisons within a spp size class
#reformat data
#calculate % change
pre.pond.wet.dry.direct.spp.pairwise <- pre.pond.wet.dry.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(pre.pond.wet.dry.direct.pairwise)) %>%
separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>%
unite("Species", c(Genus, Species), sep = "_") %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, ".", `Pond_Wet_Pre-Invasion`, `Pond_Dry_Pre-Invasion`) %>%
rename(`Wet < Dry` = ".") %>%
distinct(Species, .keep_all = T) %>%
separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>%
separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>%
pivot_wider(names_from = "Season", values_from = "Mode") %>%
mutate(#`Pre > Post` = 1 - `Pre < Post`,
Direction = case_when(
`Wet < Dry` >= 0.95 ~ "Increase",
`Wet < Dry` < 0.05 ~ "Decrease",
`Wet < Dry` > 0.05 | `Wet < Dry` < 0.95 ~ "No Change"),
`Percent Change` = ((Dry-Wet)/Wet)*100,
Species = case_when(
Species == "A_nat" ~ "A. natalis",
Species == "B_bel" ~ "B. belizanus",
Species == "C_bat" ~ "C. batrachus",
#Species == "C_var" ~ "C. variegatus",
#Species == "C_uro" ~ "M. urophthalmus",
Species == "E_eve" ~ "E. evergladei",
#Species == "E_glo" ~ "E. gloriosus",
Species == "F_chr" ~ "F. chrysotus",
#Species == "F_con" ~ "F. confluentus",
Species == "G_hol" ~ "G. holbrooki",
Species == "H_for" ~ "H. formosa",
#Species == "J_flo" ~ "J. floridae",
#Species == "L_goo" ~ "L. goodei",
Species == "L_gul" ~ "L. gulosus",
Species == "L_mac" ~ "L. macrochirus",
Species == "L_mar" ~ "L. marginatus",
#Species == "L_mic" ~ "L. microlophus",
Species == "L_pun" ~ "L. punctatus",
Species == "L_pla" ~ "L. platyrhincus",
#Species == "M_sal" ~ "M. salmoides",
Species == "N_gyr" ~ "N. gyrinus"
#Species == "P_lat" ~ "P. latipinna"
#Species == "T_mar" ~ "P. mariae"
)) %>%
filter(!is.na(`Percent Change`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Invasion_Status, Habitat, Species, `Size Class`, Wet, Dry, `Wet < Dry`,
Direction, `Percent Change`)2.4.4.5 Post-Invasion Marsh vs Pond Wet Seaosn Comparisons
#reformat data to have only Post-invasion wet-season pond-marsh comparisons
#diagonal will be comparisons within a size class
post.marsh.pond.wet.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Marsh", Season == "Wet") %>%
arrange(Species) %>%
select(-c(Habitat)) %>%
unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
arrange(Species) %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Pond", Season == "Wet") %>%
select(-c(Habitat)) %>%
unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>%
filter(Invasion_Season_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Season_Spp") %>%
filter(Invasion_Season_Spp %in% colnames(.)) %>%
mutate(Habitat = "Pond") %>%
unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>%
column_to_rownames(var = "Habitat_Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
mutate(Habitat = "Marsh") %>%
rownames_to_column(var = "Invasion_Season_Spp") %>%
unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>%
column_to_rownames(var = "Habitat_Invasion_Season_Spp")
#isolate comparisons within a spp size class
#reformat data
#calculate % change
post.marsh.pond.wet.direct.spp.pairwise <- post.marsh.pond.wet.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(post.marsh.pond.wet.direct.pairwise)) %>%
separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>%
unite("Species", c(Genus, Species), sep = "_") %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, ".", `Marsh_Wet_Post-Invasion`, `Pond_Wet_Post-Invasion`) %>%
rename(`Marsh < Pond` = ".") %>%
distinct(Species, .keep_all = T) %>%
separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>%
separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>%
pivot_wider(names_from = "Habitat", values_from = "Mode") %>%
mutate(#`Post > Post` = 1 - `Post < Post`,
Direction = case_when(
`Marsh < Pond` >= 0.95 ~ "Increase",
`Marsh < Pond` < 0.05 ~ "Decrease",
`Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-Marsh)/Marsh)*100,
Species = case_when(
#Species == "A_nat" ~ "A. natalis",
Species == "C_uro" ~ "M. urophthalmus",
#Species == "E_eve" ~ "E. evergladei",
#Species == "E_glo" ~ "E. gloriosus",
Species == "E_suc" ~ "E. sucetta",
Species == "F_chr" ~ "F. chrysotus",
#Species == "F_con" ~ "F. confluentus",
Species == "G_hol" ~ "G. holbrooki",
Species == "H_for" ~ "H. formosa",
Species == "H_let" ~ "H. letourneuxi",
Species == "J_flo" ~ "J. floridae",
Species == "L_goo" ~ "L. goodei",
Species == "L_gul" ~ "L. gulosus",
Species == "L_mac" ~ "L. macrochirus",
#Species == "L_mar" ~ "L. marginatus",
Species == "L_mic" ~ "L. microlophus",
Species == "L_pun" ~ "L. punctatus",
#Species == "L_pla" ~ "L. platyrhincus",
#Species == "M_sal" ~ "M. salmoides",
Species == "N_gyr" ~ "N. gyrinus",
Species == "O_aur" ~ "O. aureus"
#Species == "P_lat" ~ "P. latipinna",
#Species == "T_mar" ~ "P. mariae"
)) %>%
filter(!is.na(`Percent Change`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Invasion_Status, Season, Species, `Size Class`, Marsh, Pond, `Marsh < Pond`,
Direction, `Percent Change`)
2.4.4.6 Post-Invasion Marsh vs Pond Dry Season Comparisons
#reformat data to have only Post-invasion Dry-season pond-marsh comparisons
#diagonal will be comparisons within a size class
post.marsh.pond.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Marsh", Season == "Dry") %>%
arrange(Species) %>%
select(-c(Habitat)) %>%
unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
arrange(Species) %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Pond", Season == "Dry") %>%
select(-c(Habitat)) %>%
unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>%
filter(Invasion_Season_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Season_Spp") %>%
filter(Invasion_Season_Spp %in% colnames(.)) %>%
mutate(Habitat = "Pond") %>%
unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>%
column_to_rownames(var = "Habitat_Invasion_Season_Spp") %>%
t() %>% as.data.frame() %>%
mutate(Habitat = "Marsh") %>%
rownames_to_column(var = "Invasion_Season_Spp") %>%
unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>%
column_to_rownames(var = "Habitat_Invasion_Season_Spp")
#isolate comparisons within a spp size class
#reformat data
#calculate % change
post.marsh.pond.dry.direct.spp.pairwise <- post.marsh.pond.dry.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(post.marsh.pond.dry.direct.pairwise)) %>%
separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>%
unite("Species", c(Genus, Species), sep = "_") %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, ".", `Marsh_Dry_Post-Invasion`, `Pond_Dry_Post-Invasion`) %>%
rename(`Marsh < Pond` = ".") %>%
distinct(Species, .keep_all = T) %>%
separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>%
separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>%
pivot_wider(names_from = "Habitat", values_from = "Mode") %>%
mutate(#`Post > Post` = 1 - `Post < Post`,
Direction = case_when(
`Marsh < Pond` >= 0.95 ~ "Increase",
`Marsh < Pond` < 0.05 ~ "Decrease",
`Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
`Percent Change` = ((Pond-Marsh)/Marsh)*100,
Species = case_when(
#Species == "A_nat" ~ "A. natalis",
#Species == "C_uro" ~ "M. urophthalmus",
#Species == "E_eve" ~ "E. evergladei",
#Species == "E_glo" ~ "E. gloriosus",
Species == "F_chr" ~ "F. chrysotus",
#Species == "F_con" ~ "F. confluentus",
Species == "G_hol" ~ "G. holbrooki",
Species == "H_for" ~ "H. formosa",
Species == "H_lit" ~ "H. littorale",
#Species == "J_flo" ~ "J. floridae",
Species == "L_goo" ~ "L. goodei",
# Species == "L_gul" ~ "L. gulosus",
#Species == "L_mac" ~ "L. macrochirus",
Species == "L_mar" ~ "L. marginatus",
#Species == "L_mic" ~ "L. microlophus",
Species == "L_pun" ~ "L. punctatus"
#Species == "L_pla" ~ "L. platyrhincus",
#Species == "M_sal" ~ "M. salmoides",
#Species == "N_gyr" ~ "N. gyrinus",
#Species == "P_lat" ~ "P. latipinna"
#Species == "T_mar" ~ "P. mariae"
)) %>%
filter(!is.na(`Percent Change`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Invasion_Status, Season, Species, `Size Class`, Marsh, Pond, `Marsh < Pond`,
Direction, `Percent Change`)2.4.4.7 Post-Invasion Wet vs Dry Marsh Comparisons
#reformat data to have only Post-invasion wet-season pond-marsh comparisons
#diagonal will be comparisons within a size class
post.marsh.wet.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Marsh", Season == "Wet") %>%
arrange(Species) %>%
select(-c(Season)) %>%
unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
arrange(Species) %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Marsh", Season == "Dry") %>%
select(-c(Season)) %>%
unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>%
filter(Invasion_Habitat_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Habitat_Spp") %>%
filter(Invasion_Habitat_Spp %in% colnames(.)) %>%
mutate(Season = "Dry") %>%
unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>%
column_to_rownames(var = "Season_Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
mutate(Season = "Wet") %>%
rownames_to_column(var = "Invasion_Habitat_Spp") %>%
unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>%
column_to_rownames(var = "Season_Invasion_Habitat_Spp")
#isolate comparisons within a spp size class
#reformat data
#calculate % change
post.marsh.wet.dry.direct.spp.pairwise <- post.marsh.wet.dry.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(post.marsh.wet.dry.direct.pairwise)) %>%
separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>%
unite("Species", c(Genus, Species), sep = "_") %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, ".", `Marsh_Wet_Post-Invasion`, `Marsh_Dry_Post-Invasion`) %>%
rename(`Wet < Dry` = ".") %>%
distinct(Species, .keep_all = T) %>%
separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>%
separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>%
pivot_wider(names_from = "Season", values_from = "Mode") %>%
mutate(#`Post > Post` = 1 - `Post < Post`,
Direction = case_when(
`Wet < Dry` >= 0.95 ~ "Increase",
`Wet < Dry` < 0.05 ~ "Decrease",
`Wet < Dry` > 0.05 | `Wet < Dry` < 0.95 ~ "No Change"),
`Percent Change` = ((Dry-Wet)/Wet)*100,
Species = case_when(
#Species == "A_nat" ~ "A. natalis",
#Species == "C_var" ~ "C. variegatus",
Species == "C_uro" ~ "M. urophthalmus",
#Species == "E_eve" ~ "E. evergladei",
Species == "E_suc" ~ "E. sucetta",
#Species == "E_glo" ~ "E. gloriosus",
Species == "F_chr" ~ "F. chrysotus",
#Species == "F_con" ~ "F. confluentus",
Species == "G_hol" ~ "G. holbrooki",
Species == "H_for" ~ "H. formosa",
Species == "H_let" ~ "H. letourneuxi",
#Species == "J_flo" ~ "J. floridae",
Species == "L_goo" ~ "L. goodei",
#Species == "L_gul" ~ "L. gulosus",
#Species == "L_mac" ~ "L. macrochirus",
#Species == "L_mar" ~ "L. marginatus",
#Species == "L_mic" ~ "L. microlophus",
Species == "L_pun" ~ "L. punctatus"
#Species == "L_pla" ~ "L. platyrhincus",
#Species == "M_sal" ~ "M. salmoides",
#Species == "N_gyr" ~ "N. gyrinus",
#Species == "P_lat" ~ "P. latipinna"
#Species == "T_mar" ~ "P. mariae"
)) %>%
filter(!is.na(`Percent Change`)) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
arrange(Species) %>%
select(Invasion_Status, Habitat, Species, `Size Class`, Wet, Dry, `Wet < Dry`,
Direction, `Percent Change`)2.4.4.8 Post-Invasion Wet vs Dry Pond Comparisons
#reformat data to have only Post-invasion wet-season pond-Pond comparisons
#diagonal will be comparisons within a size class
post.pond.wet.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Pond", Season == "Wet") %>%
arrange(Species) %>%
select(-c(Season)) %>%
unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Status_Spp") %>%
filter(grepl("Post", Invasion_Status_Spp)) %>%
separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>%
arrange(Species) %>%
mutate(Invasion_Status = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>%
separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>%
filter(Habitat == "Pond", Season == "Dry") %>%
select(-c(Season)) %>%
unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>%
filter(Invasion_Habitat_Spp %in% colnames(.)) %>%
column_to_rownames(var = "Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Invasion_Habitat_Spp") %>%
filter(Invasion_Habitat_Spp %in% colnames(.)) %>%
mutate(Season = "Dry") %>%
unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>%
column_to_rownames(var = "Season_Invasion_Habitat_Spp") %>%
t() %>% as.data.frame() %>%
mutate(Season = "Wet") %>%
rownames_to_column(var = "Invasion_Habitat_Spp") %>%
unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>%
column_to_rownames(var = "Season_Invasion_Habitat_Spp")
#isolate comparisons within a spp size class
#reformat data
#calculate % change
post.pond.wet.dry.direct.spp.pairwise <- post.pond.wet.dry.direct.pairwise %>% as.matrix() %>%
diag() %>% as.data.frame() %>%
mutate(Species = rownames(post.pond.wet.dry.direct.pairwise)) %>%
separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>%
unite("Species", c(Genus, Species), sep = "_") %>%
merge(., pre.post.modes.wide, by = "Species") %>%
select(Species, ".", `Pond_Wet_Post-Invasion`, `Pond_Dry_Post-Invasion`) %>%
rename(`Wet < Dry` = ".") %>%
distinct(Species, .keep_all = T) %>%
separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>%
pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>%
separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>%
pivot_wider(names_from = "Season", values_from = "Mode") %>%
mutate(#`Post > Post` = 1 - `Post < Post`,
Direction = case_when(
`Wet < Dry` >= 0.95 ~ "Increase",
`Wet < Dry` < 0.05 ~ "Decrease",
`Wet < Dry` > 0.05 | `Wet < Dry` < 0.95 ~ "No Change"),
`Percent Change` = ((Dry-Wet)/Wet)*100,
Species = case_when(
#Species == "A_nat" ~ "A. natalis",
#Species == "B_bel" ~ "B. belizanus",
#Species == "C_bat" ~ "C. batrachus",
#Species == "C_var" ~ "C. variegatus",
#Species == "C_uro" ~ "M. urophthalmus",
#Species == "E_eve" ~ "E. evergladei",
#Species == "E_glo" ~ "E. gloriosus",
Species == "F_chr" ~ "F. chrysotus",
#Species == "F_con" ~ "F. confluentus",
Species == "G_hol" ~ "G. holbrooki",
Species == "H_for" ~ "H. formosa",
#Species == "J_flo" ~ "J. floridae",
Species == "L_goo" ~ "L. goodei",
Species == "L_gul" ~ "L. gulosus",
#Species == "L_mac" ~ "L. macrochirus",
Species == "L_mar" ~ "L. marginatus",
#Species == "L_mic" ~ "L. microlophus",
Species == "L_pun" ~ "L. punctatus",
Species == "L_pla" ~ "L. platyrhincus",
#Species == "M_sal" ~ "M. salmoides",
#Species == "N_gyr" ~ "N. gyrinus"
Species == "P_lat" ~ "P. latipinna"
#Species == "T_mar" ~ "P. mariae"
)) %>%
filter(!is.na(`Percent Change`)) %>%
arrange(Species) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
select(Invasion_Status, Habitat, Species, `Size Class`, Wet, Dry, `Wet < Dry`,
Direction, `Percent Change`)
2.4.5 Plot Pre- vs Post-Invasion
2.4.5.1 Trophic Niche Ellipses
Reformat data for plotting
pre.post.plot.data <- pre.post.SEA.B %>% as.data.frame() %>%
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pivot_longer(everything(), names_to = c("Invasion Status", "Species"), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument names_sep = "[//.]", values_to = "SEA_B") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(`Invasion Status`, into = c("Habitat", "Season", "Invasion Status"), "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite("Habitat_Season", c(Habitat, Season), sep = "_") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument unite(Habitat_Season_Spp, c(Habitat_Season, Species), sep = ".") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Habitat_Season_Spp, into = c("Habitat_Season", "Species"), sep = "[//.]") %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument separate(Habitat_Season, into = c("Habitat", "Season"), sep = "_")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentBox plots
#plot these dataError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentwindowsFonts(Times = windowsFont("Times New Roman"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument(pre.post.seab<-ggplot(pre.post.plot.data, aes(x = Species, y = SEA_B, fill = `Invasion Status`))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1, alpha = 0.5)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument facet_grid(Habitat~Season, scales = "free_x")+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_color_viridis_d(aesthetics = "fill", option = "D", end = 0.75)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument scale_y_continuous(limits = c(0, 2.5), expand = c(0.01, 0))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme_bw(base_size = 12)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument xlab(NULL)+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ylab(expression("Stomach Content Niche Area - SEA"["b"]))+Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument theme(text = element_text(family = "Times", size = 12),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #panel.border = element_blank(), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #panel.grid.major = element_blank(),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument #panel.grid.minor = element_blank(), Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.line = element_line(colour = "black"),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.ticks.x = element_line(size = 0.05),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument legend.title = element_blank(), legend.text = element_text(size = 8),Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument legend.key.size = unit(0.75, "line"))Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)
ggsave(filename = "Figures/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAb.tiff", Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument plot = pre.post.seab, Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument dpi = 300, width = 200, height = 100, units = "mm")
2.4.5.2 Frequency Histogram of Effect Sizes
2.4.5.2.1 Invasion Effect Sizes
Reformat data for plotting
pre.post.stat.diff.guild <- pre.post.direct.spp.pairwise %>%
#filter(`Pre < Post` >= 0.95 | `Pre < Post` <= 0.05) %>%
mutate(Guild = case_when(
Species %in% c("P. latipinna", "P. mariae") ~ "Detritivore",
Species %in% c("E. evergladei", "E. gloriosus", "F. chrysotus", "L. goodei", "L. macrochirus",
"L. marginatus", "L. microlophus") ~ "Invertivore",
Species == "L. gulosus" ~ "Mesopredator",
Species %in% c("M. salmoides", "L. platyrhincus") ~ "Top Predator",
Species %in% c("A. natalis", "G. holbrooki", "H. formosa", "J. floridae", "L. punctatus",
"M. urophthalmus") ~ "Omnivore"
),
`Statistically Different` = case_when(
`Pre < Post` >= 0.95 | `Pre < Post` <= 0.05 ~ "Yes",
T ~ "No"
))
Generate Plot
windowsFonts(Times = windowsFont("Times New Roman"))
(pre.post.stat.diff.freq.hist <- ggplot(data = pre.post.stat.diff.guild,
aes(x = `Percent Change`, fill = Guild, color = `Statistically Different`))+
geom_histogram(bins = 20)+
facet_grid(Habitat ~ Season, scales = "free")+
scale_fill_viridis_d(option = "C")+
scale_color_viridis_d(option = "B", direction = -1)+
scale_y_continuous(expand = c(0,0))+
#scale_x_continuous()
ylab("Number of Species Size Classes")+
xlab("Trophic Dispersion - Percent Change in Trophic Niche Area")+
theme_bw(base_size = 12)+
theme(text = element_text(family = "Times", size = 12),
#axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),
#panel.border = element_blank(),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),
axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),
#legend.title = element_blank(),
legend.text = element_text(size = 8)#,
#legend.key.size = unit(0.75, "line")
)
)
ggsave(filename = "Figures/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAb_effect_size_freq_hist.tiff",
plot = pre.post.stat.diff.freq.hist,
dpi = 300, width = 200, height = 100, units = "mm")2.4.5.2.2 Pre/Post Habitat Shift Effect Sizes
pre.post.habitat.shifts <- bind_rows(pre.marsh.pond.wet.direct.spp.pairwise,
pre.marsh.pond.Dry.direct.spp.pairwise,
post.marsh.pond.wet.direct.spp.pairwise,
post.marsh.pond.dry.direct.spp.pairwise) %>%
mutate(Guild = case_when(
Species %in% c("P. latipinna", "P. mariae", "O. aureus") ~ "Detritivore",
Species %in% c("E. evergladei", "E. gloriosus", "F. chrysotus", "L. goodei", "L. macrochirus",
"L. marginatus", "L. microlophus", "E. sucetta", "F. confluentus") ~ "Invertivore",
Species == "L. gulosus" ~ "Mesopredator",
Species %in% c("M. salmoides", "L. platyrhincus") ~ "Top Predator",
Species %in% c("A. natalis", "G. holbrooki", "H. formosa", "J. floridae", "L. punctatus",
"M. urophthalmus", "N. gyrinus", "H. letourneuxi", "H. littorale") ~ "Omnivore"
)) %>%
mutate(Invasion_Status = case_when(
Invasion_Status == "Pre" ~ "Pre-Invasion",
Invasion_Status == "Post" ~ "Post-Invasion"
)) %>%
rename(`Invasion Status` = Invasion_Status) %>%
filter(`Marsh < Pond` >= 0.95 | `Marsh < Pond` <= 0.05)
windowsFonts(Times = windowsFont("Times New Roman"))
(pre.post.habitat.freq.hist <- ggplot(data = pre.post.habitat.shifts, aes(x = `Percent Change`, fill = Guild))+
geom_histogram(bins = 20)+
facet_grid(`Invasion Status` ~ Season, scales = "free")+
scale_fill_viridis_d(option = "C")+
scale_y_continuous(expand = c(0,0))+
#scale_x_continuous()
ylab("Frequency")+
theme_bw(base_size = 12)+
theme(text = element_text(family = "Times", size = 12),
#axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),
#panel.border = element_blank(),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),
axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),
legend.title = element_blank(), legend.text = element_text(size = 8)#,
#legend.key.size = unit(0.75, "line")
)
)
ggsave(filename = "Figures/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAb_habitat_effect_size_freq_hist.tiff",
plot = pre.post.habitat.freq.hist,
dpi = 300, width = 200, height = 100, units = "mm")2.4.5.2.3 Pre/Post Season Shift Effect Sizes
pre.post.season.shifts <- bind_rows(pre.marsh.wet.dry.direct.spp.pairwise,
pre.pond.wet.dry.direct.spp.pairwise,
post.marsh.wet.dry.direct.spp.pairwise,
post.pond.wet.dry.direct.spp.pairwise) %>%
mutate(Guild = case_when(
Species %in% c("P. latipinna", "P. mariae", "O. aureus") ~ "Detritivore",
Species %in% c("E. evergladei", "E. gloriosus", "F. chrysotus", "L. goodei", "L. macrochirus",
"L. marginatus", "L. microlophus", "E. sucetta", "F. confluentus") ~ "Invertivore",
Species == "L. gulosus" ~ "Mesopredator",
Species %in% c("M. salmoides", "L. platyrhincus", "B. belizanus") ~ "Top Predator",
Species %in% c("A. natalis", "G. holbrooki", "H. formosa", "J. floridae", "L. punctatus",
"M. urophthalmus", "N. gyrinus", "H. letourneuxi", "H. littorale",
"C. batrachus", "C. variegatus") ~ "Omnivore"
)) %>%
mutate(Invasion_Status = case_when(
Invasion_Status == "Pre" ~ "Pre-Invasion",
Invasion_Status == "Post" ~ "Post-Invasion"
)) %>%
rename(`Invasion Status` = Invasion_Status) %>%
filter(`Wet < Dry` >= 0.95 | `Wet < Dry` <= 0.05)
windowsFonts(Times = windowsFont("Times New Roman"))
(pre.post.season.freq.hist <- ggplot(data = pre.post.season.shifts, aes(x = `Percent Change`, fill = Guild))+
geom_histogram(bins = 20)+
facet_grid(`Invasion Status` ~ Habitat, scales = "free")+
scale_fill_viridis_d(option = "C")+
scale_y_continuous(expand = c(0,0))+
#scale_x_continuous()
ylab("Frequency")+
theme_bw(base_size = 12)+
theme(text = element_text(family = "Times", size = 12),
#axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),
#panel.border = element_blank(),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),
axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),
legend.title = element_blank(), legend.text = element_text(size = 8)#,
#legend.key.size = unit(0.75, "line")
)
)
ggsave(filename = "Figures/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAb_season_effect_size_freq_hist.tiff",
plot = pre.post.season.freq.hist,
dpi = 300, width = 200, height = 100, units = "mm")2.4.6 Summary Metrics for Shifts in Trophic Niche Area
#mean and sd of different shiftsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#pre-invasion habitatError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.habitat.shifts <- pre.post.habitat.shifts %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Invasion Status` == "Pre-Invasion")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentmean(pre.habitat.shifts$`Percent Change`)[1] 170.5405Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsd(pre.habitat.shifts$`Percent Change`)[1] 343.6222Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#post-invasion habitatError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpost.habitat.shifts <- pre.post.habitat.shifts %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Invasion Status` == "Post-Invasion")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentmean(post.habitat.shifts$`Percent Change`)[1] 214.7267Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsd(post.habitat.shifts$`Percent Change`)[1] 393.4058Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#pre-invasion frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentnrow(pre.habitat.shifts)/nrow(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument bind_rows(pre.marsh.pond.wet.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.marsh.pond.Dry.direct.spp.pairwise)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)[1] 0.7Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#post-invasion frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentnrow(post.habitat.shifts)/nrow(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument bind_rows(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.marsh.pond.wet.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.marsh.pond.dry.direct.spp.pairwise)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)[1] 0.3913043Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument
Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#pre-invasion seasonError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.season.shifts <- pre.post.season.shifts %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Invasion Status` == "Pre-Invasion")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentmean(pre.season.shifts$`Percent Change`)[1] 121.6241Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsd(pre.season.shifts$`Percent Change`)[1] 299.0622Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#post-invasion seasonError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpost.season.shifts <- pre.post.season.shifts %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Invasion Status` == "Post-Invasion")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentmean(post.season.shifts$`Percent Change`)[1] 35.02923Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsd(post.season.shifts$`Percent Change`)[1] 206.801Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#pre-invasion frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentnrow(pre.season.shifts)/nrow(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument bind_rows(pre.marsh.wet.dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.pond.wet.dry.direct.spp.pairwise)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)[1] 0.5116279Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#post-invasion frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.pond.wet.dry.direct.spp.pairwise)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)[1] 0.5416667Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#overall spatiotemporal mean percent changeError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentspatiotemporal.changes <- bind_rows(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.post.season.shifts,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.post.habitat.shiftsError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#average magnitudeError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentmean(spatiotemporal.changes$`Percent Change`)[1] 133Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsd(spatiotemporal.changes$`Percent Change`)[1] 311.4584Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentnrow(spatiotemporal.changes)/nrow(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument bind_rows(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.marsh.wet.dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.pond.wet.dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.marsh.wet.dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.pond.wet.dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.marsh.pond.wet.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.marsh.pond.Dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.marsh.pond.wet.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.marsh.pond.dry.direct.spp.pairwiseError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument )Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument)[1] 0.5416667Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#post-invasion spatiotemporalError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#magnitudeError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpost.spatiotemporal.changes <- spatiotemporal.changes %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Invasion Status` == "Post-Invasion")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentmean(post.spatiotemporal.changes$`Percent Change`)[1] 108.5418Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsd(post.spatiotemporal.changes$`Percent Change`)[1] 302.6136Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentnrow(post.spatiotemporal.changes)/Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument nrow(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument bind_rows(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.marsh.wet.dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.pond.wet.dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.marsh.pond.wet.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument post.marsh.pond.dry.direct.spp.pairwiseError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ) Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument )[1] 0.4680851Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#pre-invasion spatiotemporalError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentpre.spatiotemporal.changes <- spatiotemporal.changes %>% Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument filter(`Invasion Status` == "Pre-Invasion")Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentmean(pre.spatiotemporal.changes$`Percent Change`)[1] 145.5135Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentsd(pre.spatiotemporal.changes$`Percent Change`)[1] 318.6816Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument#frequencyError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argumentnrow(pre.spatiotemporal.changes)/Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument nrow(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument bind_rows(Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.marsh.wet.dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.pond.wet.dry.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.marsh.pond.wet.direct.spp.pairwise,Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument pre.marsh.pond.Dry.direct.spp.pairwiseError in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument ) Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first argument )[1] 0.5890411Error in exists(cacheKey, where = .rs.WorkingDataEnv, inherits = FALSE) :
invalid first 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e)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.marsh.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
write.csv(srs.marsh.wet.dry.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/srs_marsh_wet_dry_SEAB_prob_diff.csv", 
          row.names = F)
```

#### SRS Near Pond Wet-Dry Comparisons
```{r}
srs.np.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

srs.np.wet.dry.direct.pairwise <- srs.np.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.np.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
write.csv(srs.np.wet.dry.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/srs_np_wet_dry_SEAB_prob_diff.csv",
          row.names = F)
```

#### SRS Pond Wet-Dry Comparisons
```{r}
srs.pond.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

srs.pond.wet.dry.direct.pairwise <- srs.pond.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.pond.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
write.csv(srs.pond.wet.dry.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/srs_pond_wet_dry_SEAB_prob_diff.csv",
          row.names = F)
```

#### TSL Marsh Wet-Dry Comparisons
```{r}
tsl.marsh.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.marsh.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

tsl.marsh.wet.dry.direct.pairwise <- tsl.marsh.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.marsh.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.marsh.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "BLUE MITE" ~ "Hydrachnidia",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "CHIRON" ~  "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CORIXI" ~ "Corixidae",
      `Size Class` == "EPHEME" ~ "Ephemeroptera",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "GERRID" ~ "Gerridae",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
write.csv(tsl.marsh.wet.dry.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/tsl_marsh_wet_dry_SEAB_prob_diff.csv",
          row.names = F)
```

#### TSL Near Pond Wet-Dry Comparisons

All TSL near-pond habitats were dry when sampled during the dry season so wet-dry comparisons could not be made

#### TSL Pond Wet-Dry Comparisons
```{r}
tsl.pond.wet.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Season, values_from = mode) %>% 
  filter(!is.na(Wet), !is.na(Dry))

tsl.pond.wet.dry.direct.pairwise <- tsl.pond.wet.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.pond.wet.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Wet"), contains("Dry"), everything()) %>% select(-c(Season)) %>% 
  rename(`Dry < Wet` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Dry < Wet` >= 0.95 ~ "Decrease",
      `Dry < Wet` < 0.05 ~ "Increase",
      `Dry < Wet` > 0.05 | `Dry < Wet` < 0.95 ~ "No Change"),
    `Percent Change` = ((Wet-Dry)/Dry)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Habitat, `Size Class`, Wet, Dry, `Dry < Wet`, `Percent Change`)

#write this out to excel
write.csv(tsl.pond.wet.dry.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/tsl_pond_wet_dry_SEAB_prob_diff.csv",
          row.names = F)
```

#### Combine Output for Seasonal Comparisons
```{r}
seasonal.pairiwse <- bind_rows(srs.marsh.wet.dry.direct.pairwise, srs.np.wet.dry.direct.pairwise, srs.pond.wet.dry.direct.pairwise,
                               tsl.marsh.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise)
write.csv(seasonal.pairiwse, 
          "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/season_pairiwise.csv",
          row.names = F)

#proportion of statistical changes in wet-dry comparisons
sum(seasonal.pairiwse$`Dry < Wet`>=0.95 | seasonal.pairiwse$`Dry < Wet`<=0.05)/nrow(seasonal.pairiwse) 

#mean and sd % change all seasonal changes
seasonal.pairiwse %>% 
  filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#break it down by habitat
#marsh
#frequency
sum(srs.marsh.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
      srs.marsh.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,
    tsl.marsh.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
      tsl.marsh.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/
  nrow(bind_rows(srs.marsh.wet.dry.direct.pairwise, tsl.marsh.wet.dry.direct.pairwise))
#magnitude
bind_rows(srs.marsh.wet.dry.direct.pairwise, tsl.marsh.wet.dry.direct.pairwise)%>% 
  filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#NP - no TSL NP
#frequency
sum(srs.np.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
      srs.np.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/
  nrow(srs.np.wet.dry.direct.pairwise)
#magnitude
bind_rows(srs.np.wet.dry.direct.pairwise)%>% 
  filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#Pond
#frequency
sum(srs.pond.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
      srs.pond.wet.dry.direct.pairwise$`Dry < Wet`<=0.05,
    tsl.pond.wet.dry.direct.pairwise$`Dry < Wet`>=0.95|
      tsl.pond.wet.dry.direct.pairwise$`Dry < Wet`<=0.05)/
  nrow(bind_rows(srs.pond.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise))
#magnitude
bind_rows(srs.pond.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise)%>% 
  filter(`Dry < Wet` >= 0.95 | `Dry < Wet` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
```
#### Seasonal Percent Change Plots
Format data for plotting
```{r}
all.season.direct.pairwise <- bind_rows(
  srs.marsh.wet.dry.direct.pairwise, srs.np.wet.dry.direct.pairwise, srs.pond.wet.dry.direct.pairwise,
  tsl.marsh.wet.dry.direct.pairwise, tsl.pond.wet.dry.direct.pairwise #no tsl.np.wet.dry.direct.pairwise
  ) %>% 
  mutate(`Size Class` = case_when(
    `Size Class` == "C.uro1" ~ "M.uro1",
    T ~ `Size Class`
  )) %>% 
  rename(`Wet < Dry` = `Dry < Wet`) %>% 
  mutate(Trophic_Guild = case_when(
    `Size Class` %in% c("P.lat2" ) ~ "Detritivore",
    `Size Class` %in% c("M.uro1", "H.let1", "G.hol1", "G.hol2", "E.suc1") ~ "Omnivore",
    `Size Class` %in% c("N.pet1", "L.goo1", "L.goo2", "H.for1", "H.for2", "F.chr1") ~ "Invertivore",
    `Size Class` %in% c("L.pun2", "L.pla1", "L.gul4", "L.gul3", "L.gul2") ~ "Mesopredator"
    ),
    Trophic_Score = case_when(
      Trophic_Guild == "Mesopredator" ~ 4,
      Trophic_Guild == "Invertivore" ~ 3,
      Trophic_Guild == "Omnivore" ~ 2,
      Trophic_Guild == "Detritivore" ~ 1
    ),
    `Size Class` = fct_reorder(`Size Class`, Trophic_Score)) %>% 
  rename(`Trophic Guild` = Trophic_Guild)
```

Percent change for seasonal changes
```{r}
windowsFonts(Times = windowsFont("Times New Roman"))

(season.percent.change.plot <- ggplot(data = all.season.direct.pairwise, 
                                      aes(y = `Size Class`, x = `Percent Change`, fill = Habitat, 
                                          color = Habitat, shape = Slough))+
    geom_point(aes(size = `Wet < Dry`))+
    scale_shape_manual(values = c(22,24))+
    scale_fill_viridis_d(end = 0.8)+
    scale_color_viridis_d(end = 0.8)+
    scale_size_continuous(breaks = c(0.05, 0.25, 0.75, 0.95))+
    facet_zoom(xlim = c(-100, 100))+
    geom_hline(yintercept = 12.5, linetype = "dashed", color = viridis(n=4, option = "A")[1])+
    geom_hline(yintercept = 6.5, linetype = "dashed", color = viridis(n=4, option = "A")[2])+
    geom_hline(yintercept = 1.5, linetype = "dashed", color = viridis(n=4, option = "A")[3])+
    theme_bw()+
    theme(text = element_text(family = "Times"))
  
)

ggsave("Figures/Stomach Contents/Trophic Niche/Post-Invasion/stomach_contents_season_percent_change_plot.tiff", 
       plot = season.percent.change.plot, dpi = 300,
       height = 250, width = 150, units = "mm")
```

Boxplots for percent change per habitat across all species
```{r}
(season.percent.change.boxplot <- ggplot(data = all.season.direct.pairwise %>% 
                                           mutate(Habitat = gsub("NP", "Near Pond", Habitat)), 
                                           aes(y = `Percent Change`, x = Habitat))+
    geom_boxplot()+
    facet_zoom(ylim = c(-100, 100))+
    scale_x_discrete(limits = c("Pond", "Near Pond", "Marsh"))+
    theme_bw()+
    theme(text = element_text(family = "Times"))
  
)
```
```{r}
all.season.direct.pairwise.long <- all.season.direct.pairwise %>% 
  pivot_longer(cols = c(Wet, Dry), names_to = "Season", values_to = "Niche Area") %>% 
  mutate(Habitat = gsub("NP", "Near Pond", Habitat))


(season.niche.area.habitat.plot <- ggplot(data = all.season.direct.pairwise.long,
                                          aes(x = Season, y = `Niche Area`))+
    geom_boxplot()+
    facet_zoom(ylim = c(0, 1))+
    facet_wrap(~Habitat)+
    scale_x_discrete(limits = c("Wet", "Dry"))
    #coord_cartesian(ylim = c(0,1))
  
)

season.niche.shifts <- all.season.direct.pairwise.long %>% 
  filter(`Wet < Dry` <= 0.05 | `Wet < Dry` >= 0.95)

season.niche.shifts %>% group_by(Habitat, Season) %>% tally()

(sc.season.niche.area.shifts.habitat.plot <- ggplot(data = season.niche.shifts,
                                          aes(x = Season, y = `Niche Area`))+
    geom_boxplot()+
    #facet_zoom(ylim = c(0, 1))+
    facet_wrap(~Habitat)+
    scale_x_discrete(limits = c("Wet", "Dry"))+
    coord_cartesian(ylim = c(0,1))+
    ylab(label = expression(Niche[SCA]))+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 8),
          axis.text.y = element_text(size = 6))
  
)

ggsave("Figures/Stomach Contents/Trophic Niche/Post-Invasion/stomach_contents_seasonal_niche_shifts_per_habitat.tiff", 
       plot = sc.season.niche.area.shifts.habitat.plot, 
       dpi = 300, height = 150, width = 250, units = "mm")

season.niche.shifts %>% group_by(Habitat, Season, `Trophic Guild`) %>% tally() %>% 
  arrange(Habitat, `Trophic Guild`)

(sc.season.niche.area.shifts.guild.habitat.plot <- ggplot(data = season.niche.shifts,
                                                 aes(x = Season, y = `Niche Area`))+
    geom_boxplot()+
    #facet_zoom(ylim = c(0, 1))+
    facet_wrap(~Habitat*`Trophic Guild`, scales = "free_y")+
    scale_x_discrete(limits = c("Wet", "Dry"))+
    #coord_cartesian(ylim = c(0,1))+
    ylab(label = expression(Niche[SCA]))+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 8),
          axis.text.y = element_text(size = 6))
  
)

ggsave("Figures/Stomach Contents/Trophic Niche/Post-Invasion/stomach_contents_seasonal_niche_shifts_guild_per_habitat.tiff", 
       plot = sc.season.niche.area.shifts.guild.habitat.plot, 
       dpi = 300, height = 150, width = 250, units = "mm")
```

### Slough Pairwise Comparisons
#### SRS-TSL Marsh Wet Season Comparisons
```{r}
slough.marsh.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.marsh.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Wet", Habitat == "Marsh") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.marsh.wet.direct.pairwise <- slough.marsh.wet.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.marsh.wet.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.marsh.wet.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
write.csv(slough.marsh.wet.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_marsh_wet_SEAB_prob_diff.csv",
          row.names = F)
```

#### SRS-TSL Near Pond Wet Comparisons
```{r}
slough.np.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.np.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Wet", Habitat == "NP") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.np.wet.direct.pairwise <- slough.np.wet.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.np.wet.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.np.wet.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
write.csv(slough.np.wet.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_np_wet_SEAB_prob_diff.xlsx",
          row.names = F)
```

#### SRS-TSL Pond Wet Comparisons
```{r}
slough.pond.wet.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.pond.wet.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Wet", Habitat == "Pond") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.pond.wet.direct.pairwise <- slough.pond.wet.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.pond.wet.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.pond.wet.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
write.csv(slough.pond.wet.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_pond_wet_SEAB_prob_diff.csv",
          row.names = F)
```

#### SRS-TSL Marsh Dry Season Comparisons
```{r}
slough.marsh.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.marsh.dry.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Dry", Habitat == "Marsh") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.marsh.dry.direct.pairwise <- slough.marsh.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.marsh.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.marsh.dry.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
write.csv(slough.marsh.dry.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_marsh_dry_SEAB_prob_diff.xlsx",
          row.names = F)
```

#### SRS-TSL Near Pond Dry Comparisons
Again, no TSL near-pond dry-season data because these habitats had completely dried by the time of our dry-season sampling

#### SRS-TSL Pond Dry Comparisons
```{r}
slough.pond.dry.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
slough.pond.dry.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Season == "Dry", Habitat == "Pond") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Slough, values_from = mode) %>% 
  filter(!is.na(SRS), !is.na(TSL))

slough.pond.dry.direct.pairwise <- slough.pond.dry.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(slough.pond.dry.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., slough.pond.dry.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("TSL"), contains("SRS"), everything()) %>% select(-c(Slough)) %>% 
  rename(`TSL < SRS` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `TSL < SRS` >= 0.95 ~ "Decrease",
      `TSL < SRS` < 0.05 ~ "Increase",
      `TSL < SRS` > 0.05 | `TSL < SRS` < 0.95 ~ "No Change"),
    `Percent Change` = ((TSL-SRS)/SRS)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Season, Habitat, `Size Class`, TSL, SRS, `TSL < SRS`, `Percent Change`)

#write this out to excel
write.csv(slough.pond.dry.direct.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_pond_dry_SEAB_prob_diff.csv",
          row.names = F)
```
#### Combine Output for Slough Comparisons
```{r}
slough.pairwise <- bind_rows(slough.marsh.dry.direct.pairwise, slough.pond.dry.direct.pairwise,
                             slough.marsh.wet.direct.pairwise, slough.np.wet.direct.pairwise, slough.pond.wet.direct.pairwise)

write.csv(slough.pairwise, 
          "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/slough_pairwise.csv",
          row.names = F)

#proportion of statistical changes in slough comparisons
sum(slough.pairwise$`TSL < SRS`>=0.95 | slough.pairwise$`TSL < SRS`<=0.05)/nrow(slough.pairwise) 

#mean and sd % change all seasonal changes
slough.pairwise %>% 
  filter(`TSL < SRS` >= 0.95 | `TSL < SRS` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
```

### Habitat Pairwise Comparisons
#### SRS Marsh-Near Pond Wet Season Comparisons
```{r}
srs.wet.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.wet.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("Marsh", "NP"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

srs.wet.marsh.np.direct.pairwise <- srs.wet.marsh.np.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.marsh.np.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.wet.marsh.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < NP` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < NP` >= 0.95 ~ "Decrease",
      `Marsh < NP` < 0.05 ~ "Increase",
      `Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
    `Percent Change` = ((NP-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)

#write this out to excel
#write.csv(srs.wet.marsh.np.direct.pairwise, 
#file = "Outputs/Stomach Contents/Trophic Niche/Post-Invasion/srs_wet_marsh_np_SEAB_prob_diff.csv",
#row.names = F
```

#### SRS Marsh-Pond Wet Comparisons
```{r}
srs.wet.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.wet.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("Marsh", "Pond"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

srs.wet.marsh.pond.direct.pairwise <- srs.wet.marsh.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.marsh.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.wet.marsh.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Decrease",
      `Marsh < Pond` < 0.05 ~ "Increase",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)
```

#### SRS Near Pond-Pond Wet Season Comparisons
```{r}
srs.wet.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.wet.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("NP", "Pond"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

srs.wet.np.pond.direct.pairwise <- srs.wet.np.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.wet.np.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.wet.np.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`NP < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `NP < Pond` >= 0.95 ~ "Decrease",
      `NP < Pond` < 0.05 ~ "Increase",
      `NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-NP)/NP)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)
```


#### SRS Marsh-Near Pond Dry Season Comparisons
```{r}
srs.dry.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.dry.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("Marsh", "NP"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

srs.dry.marsh.np.direct.pairwise <- srs.dry.marsh.np.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.marsh.np.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.dry.marsh.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < NP` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < NP` >= 0.95 ~ "Decrease",
      `Marsh < NP` < 0.05 ~ "Increase",
      `Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
    `Percent Change` = ((NP-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)
```

#### SRS Marsh-Pond Dry Season Comparisons
```{r}
srs.dry.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.dry.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("Marsh", "Pond"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

srs.dry.marsh.pond.direct.pairwise <- srs.dry.marsh.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.marsh.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.dry.marsh.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Decrease",
      `Marsh < Pond` < 0.05 ~ "Increase",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PLASCA" ~ "Planorbella spp.",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)
```

#### SRS Near Pond-Pond Dry Season Comparisons
```{r}
srs.dry.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
srs.dry.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "SRS", Habitat %in%  c("NP", "Pond"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

srs.dry.np.pond.direct.pairwise <- srs.dry.np.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(srs.dry.np.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., srs.dry.np.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`NP < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `NP < Pond` >= 0.95 ~ "Decrease",
      `NP < Pond` < 0.05 ~ "Increase",
      `NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-NP)/NP)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)
```

#### TSL Marsh-Near Pond Wet Season Comparisons
```{r}
tsl.wet.marsh.np.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.wet.marsh.np.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("Marsh", "NP"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

tsl.wet.marsh.np.direct.pairwise <- tsl.wet.marsh.np.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.marsh.np.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.wet.marsh.np.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("NP"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < NP` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < NP` >= 0.95 ~ "Decrease",
      `Marsh < NP` < 0.05 ~ "Increase",
      `Marsh < NP` > 0.05 | `Marsh < NP` < 0.95 ~ "No Change"),
    `Percent Change` = ((NP-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BLUE MITE" ~ "Hydrachnidia",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COLEOA" ~ "Coleoptera",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "GERRID" ~ "Gerridae",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PLASCA" ~ "Planorbella spp.",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, NP, `Marsh < NP`, `Percent Change`)
```

#### TSL Marsh-Pond Wet Season Comparisons
```{r}
tsl.wet.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.wet.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("Marsh", "Pond"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

tsl.wet.marsh.pond.direct.pairwise <- tsl.wet.marsh.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.marsh.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.wet.marsh.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Decrease",
      `Marsh < Pond` < 0.05 ~ "Increase",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)
```

#### TSL NP-Pond Wet Season Comparisons
```{r}
tsl.wet.np.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.wet.np.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("NP", "Pond"), Season == "Wet") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

tsl.wet.np.pond.direct.pairwise <- tsl.wet.np.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.wet.np.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.wet.np.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("NP"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`NP < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `NP < Pond` >= 0.95 ~ "Decrease",
      `NP < Pond` < 0.05 ~ "Increase",
      `NP < Pond` > 0.05 | `NP < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-NP)/NP)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, NP, Pond, `NP < Pond`, `Percent Change`)
```

#### TSL Marsh-Near Pond Dry Season Comparisons
No dry-season TSL near-pond data

#### TSL Marsh-Pond Dry Season Comparisons
```{r}
tsl.dry.marsh.pond.pairwise <- SEA.B.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  #unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "SizeClass") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "SizeClass"), sep = 3) %>% 
  mutate(SizeClass = gsub("^.", "", SizeClass)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(SizeClass %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, SizeClass, matches(.$SizeClass)) %>% 
  unite(Slough_Habitat_Season_SizeClass, c("Slough", "Habitat", "Season", "SizeClass"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_SizeClass")

#format modes to bind with pairwise probs
tsl.dry.marsh.pond.modes.wide <- SEA.B.modes.matrix.t %>% as.data.frame() %>% 
  rownames_to_column(var = "Slough_Habitat_Season_SizeClass") %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season_SizeClass"), sep = "_") %>%
  separate(Season_SizeClass, into = c("Season", "Size Class"), sep = 3) %>% 
  mutate(`Size Class` = gsub("^.", "", `Size Class`)) %>% 
  mutate(Slough  = gsub("\\[[[0-9]+] ", "", Slough)) %>% 
  filter(Slough == "TSL", Habitat %in%  c("Marsh", "Pond"), Season == "Dry") %>% 
  pivot_wider(id_cols = `Size Class`, names_from = Habitat, values_from = mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

tsl.dry.marsh.pond.direct.pairwise <- tsl.dry.marsh.pond.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_SizeClass = rownames(tsl.dry.marsh.pond.pairwise)) %>% 
  separate(Slough_Habitat_Season_SizeClass, into = c("Slough", "Habitat", "Season", "Size Class"), sep = "_") %>% 
  filter(!`Size Class` %in% c("Green Algae", "FLOC", "PERMAT", "PEREPI")) %>% 
  merge(., tsl.dry.marsh.pond.modes.wide, by = "Size Class") %>% 
  select(`Size Class`, contains("Marsh"), contains("Pond"), everything()) %>% select(-c(Habitat)) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Decrease",
      `Marsh < Pond` < 0.05 ~ "Increase",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    `Size Class` = case_when(
      `Size Class` == "AMPHIPOD" ~ "Amphipoda",
      `Size Class` == "BELSPP" ~ "Belostoma spp.",
      `Size Class` == "BRAGRA" ~ "B. gravida",
      `Size Class` == "COPEPOD" ~ "Copepoda",
      `Size Class` == "CHIRON" ~ "Chironomidae",
      `Size Class` == "COENAG" ~ "Coenagrionidae",
      `Size Class` == "CELSPP" ~ "Celithimus spp.",
      `Size Class` == "ERYSIM" ~ "E. simplicicollis",
      `Size Class` == "LIBINC" ~ "L. incesta",
      `Size Class` == "PALPAL" ~ "P. paludosus",
      `Size Class` == "PELFEM" ~ "P. femoratus",
      `Size Class` == "PLASCA" ~ "Planorbella spp.",
      `Size Class` == "PROFAL" ~ "P. fallax",
      `Size Class` == "PROALL" ~ "P. alleni",
      `Size Class` == "PROSPP" ~ "Procambarus spp.",
      TRUE ~ `Size Class`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Slough, Season, `Size Class`, Marsh, Pond, `Marsh < Pond`, `Percent Change`)

```

#### TSL Near Pond-Pond Dry Season Comparisons
No dry-season TSL near-pond data

#### Combine Habitat Comparisons
```{r}
#Marsh-NP
marsh.np.comparisons <- bind_rows(srs.wet.marsh.np.direct.pairwise, srs.dry.marsh.np.direct.pairwise, 
                                  tsl.wet.marsh.np.direct.pairwise)

#proportion of statistical changes in marsh-np comparisons
sum(marsh.np.comparisons$`Marsh < NP`>=0.95 | marsh.np.comparisons$`Marsh < NP`<=0.05)/nrow(marsh.np.comparisons) 

#mean and sd % change
marsh.np.comparisons %>% 
  filter(`Marsh < NP` >= 0.95 | `Marsh < NP` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean,sd = sd))

#Marsh-Pond
marsh.pond.comparisons <- bind_rows(srs.wet.marsh.pond.direct.pairwise, srs.dry.marsh.pond.direct.pairwise, 
                                    tsl.wet.marsh.pond.direct.pairwise, tsl.dry.marsh.pond.direct.pairwise)

#proportion of statistical changes in marsh-pond comparisons
sum(marsh.pond.comparisons$`Marsh < Pond`>=0.95 | marsh.pond.comparisons$`Marsh < Pond`<=0.05)/nrow(marsh.pond.comparisons) 

#mean and sd % change
marsh.pond.comparisons %>% 
  filter(`Marsh < Pond` >= 0.95 | `Marsh < Pond` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))


#NP-Pond
np.pond.comparisons <- bind_rows(srs.wet.np.pond.direct.pairwise, srs.dry.np.pond.direct.pairwise, 
                                 tsl.wet.np.pond.direct.pairwise)

#proportion of statistical changes in marsh-np comparisons
sum(np.pond.comparisons$`NP < Pond`>=0.95 | np.pond.comparisons$`NP < Pond`<=0.05)/nrow(np.pond.comparisons) 

#mean and sd % change
np.pond.comparisons %>% 
  filter(`NP < Pond` >= 0.95 | `NP < Pond` <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))

#combine into a list and export to Excel
habitat.comparisons <- list("Marsh-NP" = marsh.np.comparisons, "Marsh-Pond" = marsh.pond.comparisons, 
                            "NP-Pond" = np.pond.comparisons)

write.xlsx(habitat.comparisons, "habitat_comparisons.xlsx")
#proportion of all habitat comparisons that were statistically different
(
  sum(marsh.np.comparisons$`Marsh < NP`>=0.95 | marsh.np.comparisons$`Marsh < NP`<=0.05)+
    sum(marsh.pond.comparisons$`Marsh < Pond`>=0.95 | marsh.pond.comparisons$`Marsh < Pond`<=0.05)+
    sum(np.pond.comparisons$`NP < Pond`>=0.95 | np.pond.comparisons$`NP < Pond`<=0.05)
)/(
  nrow(marsh.np.comparisons)+nrow(marsh.pond.comparisons)+nrow(np.pond.comparisons)
)

#mean sd % change for all habitat comparisons
bind_rows(
  marsh.np.comparisons %>% rename(Comparison = `Marsh < NP`),
  marsh.pond.comparisons %>% rename(Comparison = `Marsh < Pond`),
  np.pond.comparisons %>% rename(Comparison = `NP < Pond`)) %>% 
  filter(Comparison >= 0.95 | Comparison <= 0.05) %>% 
  mutate(`Percent Change` = abs(`Percent Change`)) %>% 
  summarize_at(.vars = "Percent Change", list(mean = mean, sd = sd))
```

# Pre- vs Post-Invasion
## Load data
This file is output from 03_diets_from_stomach_contents.Rmd
```{r}
pre.post.invasion.stomachs <- read.csv("Outputs/Stomach Contents/Diet/Pre-vs-Post-Invasion/pre_post_nmds_consumers.csv") %>% 
  mutate(size_class = gsub("[//.]", "_", size_class))
```
## Format data for SIBER
```{r}
pre.post.siber.data <- pre.post.invasion.stomachs %>% 
  unite(community, c("Habitat", "Season", "Invasion_Status"), sep = "_") %>% 
  rename(group = size_class, iso1 = MDS1, iso2 = MDS2) %>% 
  select(iso1, iso2, group, community) %>% 
  group_by(group, community) %>% 
  filter(n()>2, sd(iso1)!=0, sd(iso2)!=0, !is.na(iso1), !is.na(iso2)) %>% 
  ungroup() %>% 
  as.data.frame() %>% 
  arrange(community, group)


#createSiberObject
pre.post.siber<-createSiberOb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post_SEAB_modes.csv")

#read back in
pre.post.SEA.B.modes.matrix<-read.csv(file = "Outputs/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv", header = T, row.names = 1)

#change colnames
colnames(pre.post.SEA.B.modes.matrix)<-names(pre.post.ellipses.posterior)
#transpose this matrix
pre.post.SEA.B.modes.matrix.t<-t(pre.post.SEA.B.modes.matrix) %>% as.data.frame()
#change the name of what is now a single column to mode
colnames(pre.post.SEA.B.modes.matrix.t)<-"Mode"
#write out the final csv over the previous mode csv
write.csv(pre.post.SEA.B.modes.matrix.t, 
          file = "Outputs/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_modes.csv")
```

### Test Convergence
```{r}
#test convergence for all ellipses
pre.post.all.files <- dir(parms$save.dir, full.names = T)

#find which onces are jags model files
pre.post.model.files <- pre.post.all.files[grep("jags_output", pre.post.all.files)]

#test convergence 
do.this <- 1 #this number indicates which file, in order, is being looked at
load(pre.post.model.files[do.this])

gelman.diag(output, multivariate = F)
gelman.plot(output, auto.layout = F)

```

### Pre- vs Post-Invasion Pairwise Comparisons
```{r}
#turn the output into a list
pre.post.SEA.B.list <- as.list(as.data.frame(pre.post.SEA.B))

#use the pairwiseComparisons function from tRophicPosition package
pre.post.pairwise <- pairwiseComparisons(pre.post.SEA.B.list, print = F)
colnames(pre.post.pairwise) <- rownames(pre.post.pairwise)
#reformat data to have only pre-invasion and post-invasion comparstomachsns
pre.post.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Pre", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  arrange(Species) %>% 
  unite("Habitat_Season_Spp", c(Habitat, Season, Species), sep = "_") %>% 
  column_to_rownames(var = "Habitat_Season_Spp") %>% 
  select(-c(Invasion_Status)) %>% 
  t() %>% as.data.frame() %>% #Pre-Invasion is now the columns
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Post", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  arrange(Species) %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  unite("Habitat_Season_Spp", c(Habitat, Season, Species), sep = "_") %>% 
  filter(Habitat_Season_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Habitat_Season_Spp") %>% 
  t() %>% as.data.frame() %>%  #Now Post-Invasion is the columns
  rownames_to_column(var = "Habitat_Season_Spp") %>% 
  filter(Habitat_Season_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Habitat_Season_Spp")
#At the end Post-Invasion is the columns and Pre-Invasion is the rows
#The diagonal has Pre vs Post-Invasion comparisons within a habitat-season for a given size class
  
  
#format modes to bind with pairwise probs
pre.post.modes.wide <- pre.post.SEA.B.modes.matrix.t %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  pivot_wider(id_cols = Species, names_from = Invasion_Status, values_from = Mode) 

pre.post.direct.spp.pairwise <- pre.post.direct.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(pre.post.direct.pairwise)) %>% 
  separate(Species, into = c("Habitat", "Season", "Genus", "Species"), sep = "_") %>% 
  unite("Species", c(Genus, Species), sep = "_") %>% 
  merge(., pre.post.modes.wide, by = "Species") %>% 
  select(Species, contains("Invasion"), everything()) %>% select(-c(Habitat, Season)) %>% 
  rename(`Pre < Post` = ".") %>% 
  distinct(Species, .keep_all = T) %>% 
  separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>% 
  pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>% 
  separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  pivot_wider(names_from = "Invasion_Status", values_from = "Mode") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Pre < Post` >= 0.95 ~ "Increase",
      `Pre < Post` < 0.05 ~ "Decrease",
      `Pre < Post` > 0.05 | `Pre < Post` < 0.95 ~ "No Change"),
    `Percent Change` = ((Post-Pre)/Pre)*100,
    Species = case_when(
      Species == "A_nat" ~ "A. natalis",
      Species == "C_uro" ~ "M. urophthalmus",
      Species == "E_eve" ~ "E. evergladei",
      Species == "E_glo" ~ "E. gloriosus",
      Species == "F_chr" ~ "F. chrysotus",
      Species == "G_hol" ~ "G. holbrooki",
      Species == "H_for" ~ "H. formosa",
      Species == "J_flo" ~ "J. floridae",
      Species == "L_goo" ~ "L. goodei",
      Species == "L_gul" ~ "L. gulosus",
      Species == "L_mac" ~ "L. macrochirus",
      Species == "L_mar" ~ "L. marginatus",
      Species == "L_mic" ~ "L. microlophus",
      Species == "L_pun" ~ "L. punctatus",
      Species == "L_pla" ~ "L. platyrhincus",
      Species == "M_sal" ~ "M. salmoides",
      Species == "P_lat" ~ "P. latipinna",
      Species == "T_mar" ~ "P. mariae")) %>% 
  filter(!is.na(`Percent Change`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  arrange(Season, Habitat, Species) %>% 
  rename("Pre-Invasion" = Pre, "Post-Invasion" = Post) %>% 
  select(Season, Habitat, Species, `Size Class`, `Pre < Post`, `Pre-Invasion`, `Post-Invasion`, 
         Direction, `Percent Change`)

#write this out to excel
write.csv(pre.post.direct.spp.pairwise, 
          file = "Outputs/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAB_prob_diff.csv",
          row.names = F)

#proportion of statistical changes in pre-post comparstomachsns
sum(pre.post.direct.spp.pairwise$`Pre < Post`>=0.95 | pre.post.direct.spp.pairwise$`Pre < Post`<=0.05)/nrow(pre.post.direct.spp.pairwise) 

#proportion of changes in the dry season
nrow(
  filter(pre.post.direct.spp.pairwise, 
         `Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Dry"))/
  nrow(
    filter(pre.post.direct.spp.pairwise, Season == "Dry")
  )

#dry season ponds
nrow(
  filter(pre.post.direct.spp.pairwise, 
         `Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Dry", Habitat == "Pond"))/
  nrow(
    filter(pre.post.direct.spp.pairwise, Season == "Dry", Habitat == "Pond")
  )

#dry season marsh
nrow(
  filter(pre.post.direct.spp.pairwise, 
         `Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Dry", Habitat == "Marsh"))/
  nrow(
    filter(pre.post.direct.spp.pairwise, Season == "Dry", Habitat == "Marsh")
  )

#proportion of changes in the Wet season
nrow(
  filter(pre.post.direct.spp.pairwise, 
         `Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Wet"))/
  nrow(
    filter(pre.post.direct.spp.pairwise, Season == "Wet")
  )

#Wet season ponds
nrow(
  filter(pre.post.direct.spp.pairwise, 
         `Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Wet", Habitat == "Pond"))/
  nrow(
    filter(pre.post.direct.spp.pairwise, Season == "Wet", Habitat == "Pond")
  )

#Wet season marsh
nrow(
  filter(pre.post.direct.spp.pairwise, 
         `Pre < Post` >= 0.95 | `Pre < Post` <= 0.05, Season == "Wet", Habitat == "Marsh"))/
  nrow(
    filter(pre.post.direct.spp.pairwise, Season == "Wet", Habitat == "Marsh")
  )
```

### Pre-Invasion Habitat and Season Comparisons
#### Pre-Invasion Marsh vs Pond Wet Season Comparisons
```{r}
#reformat data to have only pre-invasion wet-season pond-marsh comparisons
#diagonal will be comparisons within a size class
pre.marsh.pond.wet.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Pre", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Marsh", Season == "Wet") %>% 
  arrange(Species) %>% 
  select(-c(Habitat)) %>% 
  unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Pre", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  arrange(Species) %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Pond", Season == "Wet") %>% 
  select(-c(Habitat)) %>% 
  unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>% 
  filter(Invasion_Season_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>%  
  rownames_to_column(var = "Invasion_Season_Spp") %>% 
  filter(Invasion_Season_Spp %in% colnames(.)) %>% 
  mutate(Habitat = "Pond") %>% 
  unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>% 
  column_to_rownames(var = "Habitat_Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>% 
  mutate(Habitat = "Marsh") %>% 
  rownames_to_column(var = "Invasion_Season_Spp") %>% 
  unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>% 
  column_to_rownames(var = "Habitat_Invasion_Season_Spp")

#isolate comparisons within a spp size class
#reformat data
#calculate % change
pre.marsh.pond.wet.direct.spp.pairwise <- pre.marsh.pond.wet.direct.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(pre.marsh.pond.wet.direct.pairwise)) %>% 
  separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>% 
  unite("Species", c(Genus, Species), sep = "_") %>% 
  merge(., pre.post.modes.wide, by = "Species") %>% 
  select(Species, ".", `Marsh_Wet_Pre-Invasion`, `Pond_Wet_Pre-Invasion`) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  distinct(Species, .keep_all = T) %>% 
  separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>% 
  pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>% 
  separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  pivot_wider(names_from = "Habitat", values_from = "Mode") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Increase",
      `Marsh < Pond` < 0.05 ~ "Decrease",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    Species = case_when(
      Species == "A_nat" ~ "A. natalis",
      #Species == "C_uro" ~ "M. urophthalmus",
      Species == "E_eve" ~ "E. evergladei",
      #Species == "E_glo" ~ "E. gloriosus",
      Species == "F_chr" ~ "F. chrysotus",
      Species == "F_con" ~ "F. confluentus",
      Species == "G_hol" ~ "G. holbrooki",
      Species == "H_for" ~ "H. formosa",
      Species == "J_flo" ~ "J. floridae",
      Species == "L_goo" ~ "L. goodei",
      Species == "L_gul" ~ "L. gulosus",
      #Species == "L_mac" ~ "L. macrochirus",
      Species == "L_mar" ~ "L. marginatus",
      #Species == "L_mic" ~ "L. microlophus",
      Species == "L_pun" ~ "L. punctatus",
      #Species == "L_pla" ~ "L. platyrhincus",
      #Species == "M_sal" ~ "M. salmoides",
      Species == "N_gyr" ~ "N. gyrinus"
      #Species == "P_lat" ~ "P. latipinna",
      #Species == "T_mar" ~ "P. mariae"
      )) %>% 
  filter(!is.na(`Percent Change`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Invasion_Status, Season, Species, `Size Class`, Marsh, Pond, `Marsh < Pond`, 
         Direction, `Percent Change`)
```

#### Pre-Invasion Marsh vs Pond Dry Season Comparisons
```{r}
#reformat data to have only pre-invasion Dry-season pond-marsh comparisons
#diagonal will be comparisons within a size class
pre.marsh.pond.Dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Pre", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Marsh", Season == "Dry") %>% 
  arrange(Species) %>% 
  select(-c(Habitat)) %>% 
  unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Pre", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  arrange(Species) %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Pond", Season == "Dry") %>% 
  select(-c(Habitat)) %>% 
  unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>% 
  filter(Invasion_Season_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>%  
  rownames_to_column(var = "Invasion_Season_Spp") %>% 
  filter(Invasion_Season_Spp %in% colnames(.)) %>% 
  mutate(Habitat = "Pond") %>% 
  unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>% 
  column_to_rownames(var = "Habitat_Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>% 
  mutate(Habitat = "Marsh") %>% 
  rownames_to_column(var = "Invasion_Season_Spp") %>% 
  unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>% 
  column_to_rownames(var = "Habitat_Invasion_Season_Spp")

#isolate comparisons within a spp size class
#reformat data
#calculate % change
pre.marsh.pond.Dry.direct.spp.pairwise <- pre.marsh.pond.Dry.direct.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(pre.marsh.pond.Dry.direct.pairwise)) %>% 
  separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>% 
  unite("Species", c(Genus, Species), sep = "_") %>% 
  merge(., pre.post.modes.wide, by = "Species") %>% 
  select(Species, ".", `Marsh_Dry_Pre-Invasion`, `Pond_Dry_Pre-Invasion`) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  distinct(Species, .keep_all = T) %>% 
  separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>% 
  pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>% 
  separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  pivot_wider(names_from = "Habitat", values_from = "Mode") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Increase",
      `Marsh < Pond` < 0.05 ~ "Decrease",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    Species = case_when(
      Species == "A_nat" ~ "A. natalis",
      #Species == "C_uro" ~ "M. urophthalmus",
      Species == "E_eve" ~ "E. evergladei",
      #Species == "E_glo" ~ "E. gloriosus",
      Species == "F_chr" ~ "F. chrysotus",
      #Species == "F_con" ~ "F. confluentus",
      Species == "G_hol" ~ "G. holbrooki",
      Species == "H_for" ~ "H. formosa",
      #Species == "J_flo" ~ "J. floridae",
      #Species == "L_goo" ~ "L. goodei",
     # Species == "L_gul" ~ "L. gulosus",
      #Species == "L_mac" ~ "L. macrochirus",
      Species == "L_mar" ~ "L. marginatus",
      #Species == "L_mic" ~ "L. microlophus",
      Species == "L_pun" ~ "L. punctatus",
      #Species == "L_pla" ~ "L. platyrhincus",
      #Species == "M_sal" ~ "M. salmoides",
      Species == "N_gyr" ~ "N. gyrinus",
      Species == "P_lat" ~ "P. latipinna"
      #Species == "T_mar" ~ "P. mariae"
    )) %>% 
  filter(!is.na(`Percent Change`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Invasion_Status, Season, Species, `Size Class`, Marsh, Pond, `Marsh < Pond`, 
         Direction, `Percent Change`)
```

#### Pre-Invasion Wet vs Dry Marsh Comparisons
```{r}
#reformat data to have only pre-invasion wet-season pond-marsh comparisons
#diagonal will be comparisons within a size class
pre.marsh.wet.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Pre", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Marsh", Season == "Wet") %>% 
  arrange(Species) %>% 
  select(-c(Season)) %>% 
  unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Pre", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  arrange(Species) %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Season)) %>% 
  unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>% 
  filter(Invasion_Habitat_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>%  
  rownames_to_column(var = "Invasion_Habitat_Spp") %>% 
  filter(Invasion_Habitat_Spp %in% colnames(.)) %>% 
  mutate(Season = "Dry") %>% 
  unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>% 
  column_to_rownames(var = "Season_Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>% 
  mutate(Season = "Wet") %>% 
  rownames_to_column(var = "Invasion_Habitat_Spp") %>% 
  unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>% 
  column_to_rownames(var = "Season_Invasion_Habitat_Spp")

#isolate comparisons within a spp size class
#reformat data
#calculate % change
pre.marsh.wet.dry.direct.spp.pairwise <- pre.marsh.wet.dry.direct.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(pre.marsh.wet.dry.direct.pairwise)) %>% 
  separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>% 
  unite("Species", c(Genus, Species), sep = "_") %>% 
  merge(., pre.post.modes.wide, by = "Species") %>% 
  select(Species, ".", `Marsh_Wet_Pre-Invasion`, `Marsh_Dry_Pre-Invasion`) %>% 
  rename(`Wet < Dry` = ".") %>% 
  distinct(Species, .keep_all = T) %>% 
  separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>% 
  pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>% 
  separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  pivot_wider(names_from = "Season", values_from = "Mode") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Wet < Dry` >= 0.95 ~ "Increase",
      `Wet < Dry` < 0.05 ~ "Decrease",
      `Wet < Dry` > 0.05 | `Wet < Dry` < 0.95 ~ "No Change"),
    `Percent Change` = ((Dry-Wet)/Wet)*100,
    Species = case_when(
      Species == "A_nat" ~ "A. natalis",
      Species == "C_var" ~ "C. variegatus",
      #Species == "C_uro" ~ "M. urophthalmus",
      Species == "E_eve" ~ "E. evergladei",
      Species == "E_glo" ~ "E. gloriosus",
      Species == "F_chr" ~ "F. chrysotus",
      Species == "F_con" ~ "F. confluentus",
      Species == "G_hol" ~ "G. holbrooki",
      Species == "H_for" ~ "H. formosa",
      Species == "J_flo" ~ "J. floridae",
      Species == "L_goo" ~ "L. goodei",
      #Species == "L_gul" ~ "L. gulosus",
      #Species == "L_mac" ~ "L. macrochirus",
      Species == "L_mar" ~ "L. marginatus",
      #Species == "L_mic" ~ "L. microlophus",
      Species == "L_pun" ~ "L. punctatus",
      #Species == "L_pla" ~ "L. platyrhincus",
      #Species == "M_sal" ~ "M. salmoides",
      Species == "N_gyr" ~ "N. gyrinus",
      Species == "P_lat" ~ "P. latipinna"
      #Species == "T_mar" ~ "P. mariae"
    )) %>% 
  filter(!is.na(`Percent Change`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Invasion_Status, Habitat, Species, `Size Class`, Wet, Dry, `Wet < Dry`, 
         Direction, `Percent Change`)
```

#### Pre-Invasion Wet vs Dry Pond Comparisons
```{r}
#reformat data to have only pre-invasion wet-season pond-Pond comparisons
#diagonal will be comparisons within a size class
pre.pond.wet.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Pre", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Pond", Season == "Wet") %>% 
  arrange(Species) %>% 
  select(-c(Season)) %>% 
  unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Pre", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  arrange(Species) %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Pond", Season == "Dry") %>% 
  select(-c(Season)) %>% 
  unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>% 
  filter(Invasion_Habitat_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>%  
  rownames_to_column(var = "Invasion_Habitat_Spp") %>% 
  filter(Invasion_Habitat_Spp %in% colnames(.)) %>% 
  mutate(Season = "Dry") %>% 
  unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>% 
  column_to_rownames(var = "Season_Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>% 
  mutate(Season = "Wet") %>% 
  rownames_to_column(var = "Invasion_Habitat_Spp") %>% 
  unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>% 
  column_to_rownames(var = "Season_Invasion_Habitat_Spp")

#isolate comparisons within a spp size class
#reformat data
#calculate % change
pre.pond.wet.dry.direct.spp.pairwise <- pre.pond.wet.dry.direct.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(pre.pond.wet.dry.direct.pairwise)) %>% 
  separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>% 
  unite("Species", c(Genus, Species), sep = "_") %>% 
  merge(., pre.post.modes.wide, by = "Species") %>% 
  select(Species, ".", `Pond_Wet_Pre-Invasion`, `Pond_Dry_Pre-Invasion`) %>% 
  rename(`Wet < Dry` = ".") %>% 
  distinct(Species, .keep_all = T) %>% 
  separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>% 
  pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>% 
  separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  pivot_wider(names_from = "Season", values_from = "Mode") %>% 
  mutate(#`Pre > Post` = 1 - `Pre < Post`,
    Direction = case_when(
      `Wet < Dry` >= 0.95 ~ "Increase",
      `Wet < Dry` < 0.05 ~ "Decrease",
      `Wet < Dry` > 0.05 | `Wet < Dry` < 0.95 ~ "No Change"),
    `Percent Change` = ((Dry-Wet)/Wet)*100,
    Species = case_when(
      Species == "A_nat" ~ "A. natalis",
      Species == "B_bel" ~ "B. belizanus",
      Species == "C_bat" ~ "C. batrachus",
      #Species == "C_var" ~ "C. variegatus",
      #Species == "C_uro" ~ "M. urophthalmus",
      Species == "E_eve" ~ "E. evergladei",
      #Species == "E_glo" ~ "E. gloriosus",
      Species == "F_chr" ~ "F. chrysotus",
      #Species == "F_con" ~ "F. confluentus",
      Species == "G_hol" ~ "G. holbrooki",
      Species == "H_for" ~ "H. formosa",
      #Species == "J_flo" ~ "J. floridae",
      #Species == "L_goo" ~ "L. goodei",
      Species == "L_gul" ~ "L. gulosus",
      Species == "L_mac" ~ "L. macrochirus",
      Species == "L_mar" ~ "L. marginatus",
      #Species == "L_mic" ~ "L. microlophus",
      Species == "L_pun" ~ "L. punctatus",
      Species == "L_pla" ~ "L. platyrhincus",
      #Species == "M_sal" ~ "M. salmoides",
      Species == "N_gyr" ~ "N. gyrinus"
      #Species == "P_lat" ~ "P. latipinna"
      #Species == "T_mar" ~ "P. mariae"
    )) %>% 
  filter(!is.na(`Percent Change`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Invasion_Status, Habitat, Species, `Size Class`, Wet, Dry, `Wet < Dry`, 
         Direction, `Percent Change`)
```

#### Post-Invasion Marsh vs Pond Wet Seaosn Comparisons
```{r}
#reformat data to have only Post-invasion wet-season pond-marsh comparisons
#diagonal will be comparisons within a size class
post.marsh.pond.wet.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Post", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Marsh", Season == "Wet") %>% 
  arrange(Species) %>% 
  select(-c(Habitat)) %>% 
  unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Post", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  arrange(Species) %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Pond", Season == "Wet") %>% 
  select(-c(Habitat)) %>% 
  unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>% 
  filter(Invasion_Season_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>%  
  rownames_to_column(var = "Invasion_Season_Spp") %>% 
  filter(Invasion_Season_Spp %in% colnames(.)) %>% 
  mutate(Habitat = "Pond") %>% 
  unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>% 
  column_to_rownames(var = "Habitat_Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>% 
  mutate(Habitat = "Marsh") %>% 
  rownames_to_column(var = "Invasion_Season_Spp") %>% 
  unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>% 
  column_to_rownames(var = "Habitat_Invasion_Season_Spp")

#isolate comparisons within a spp size class
#reformat data
#calculate % change
post.marsh.pond.wet.direct.spp.pairwise <- post.marsh.pond.wet.direct.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(post.marsh.pond.wet.direct.pairwise)) %>% 
  separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>% 
  unite("Species", c(Genus, Species), sep = "_") %>% 
  merge(., pre.post.modes.wide, by = "Species") %>% 
  select(Species, ".", `Marsh_Wet_Post-Invasion`, `Pond_Wet_Post-Invasion`) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  distinct(Species, .keep_all = T) %>% 
  separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>% 
  pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>% 
  separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  pivot_wider(names_from = "Habitat", values_from = "Mode") %>% 
  mutate(#`Post > Post` = 1 - `Post < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Increase",
      `Marsh < Pond` < 0.05 ~ "Decrease",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    Species = case_when(
      #Species == "A_nat" ~ "A. natalis",
      Species == "C_uro" ~ "M. urophthalmus",
      #Species == "E_eve" ~ "E. evergladei",
      #Species == "E_glo" ~ "E. gloriosus",
      Species == "E_suc" ~ "E. sucetta",
      Species == "F_chr" ~ "F. chrysotus",
      #Species == "F_con" ~ "F. confluentus",
      Species == "G_hol" ~ "G. holbrooki",
      Species == "H_for" ~ "H. formosa",
      Species == "H_let" ~ "H. letourneuxi",
      Species == "J_flo" ~ "J. floridae",
      Species == "L_goo" ~ "L. goodei",
      Species == "L_gul" ~ "L. gulosus",
      Species == "L_mac" ~ "L. macrochirus",
      #Species == "L_mar" ~ "L. marginatus",
      Species == "L_mic" ~ "L. microlophus",
      Species == "L_pun" ~ "L. punctatus",
      #Species == "L_pla" ~ "L. platyrhincus",
      #Species == "M_sal" ~ "M. salmoides",
      Species == "N_gyr" ~ "N. gyrinus",
      Species == "O_aur" ~ "O. aureus"
      #Species == "P_lat" ~ "P. latipinna",
      #Species == "T_mar" ~ "P. mariae"
    )) %>% 
  filter(!is.na(`Percent Change`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Invasion_Status, Season, Species, `Size Class`, Marsh, Pond, `Marsh < Pond`, 
         Direction, `Percent Change`)

```

#### Post-Invasion Marsh vs Pond Dry Season Comparisons
```{r}
#reformat data to have only Post-invasion Dry-season pond-marsh comparisons
#diagonal will be comparisons within a size class
post.marsh.pond.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Post", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Marsh", Season == "Dry") %>% 
  arrange(Species) %>% 
  select(-c(Habitat)) %>% 
  unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Post", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  arrange(Species) %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Pond", Season == "Dry") %>% 
  select(-c(Habitat)) %>% 
  unite("Invasion_Season_Spp", c(Invasion_Status, Season, Species), sep = "_") %>% 
  filter(Invasion_Season_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>%  
  rownames_to_column(var = "Invasion_Season_Spp") %>% 
  filter(Invasion_Season_Spp %in% colnames(.)) %>% 
  mutate(Habitat = "Pond") %>% 
  unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>% 
  column_to_rownames(var = "Habitat_Invasion_Season_Spp") %>% 
  t() %>% as.data.frame() %>% 
  mutate(Habitat = "Marsh") %>% 
  rownames_to_column(var = "Invasion_Season_Spp") %>% 
  unite("Habitat_Invasion_Season_Spp", c(Habitat, Invasion_Season_Spp)) %>% 
  column_to_rownames(var = "Habitat_Invasion_Season_Spp")

#isolate comparisons within a spp size class
#reformat data
#calculate % change
post.marsh.pond.dry.direct.spp.pairwise <- post.marsh.pond.dry.direct.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(post.marsh.pond.dry.direct.pairwise)) %>% 
  separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>% 
  unite("Species", c(Genus, Species), sep = "_") %>% 
  merge(., pre.post.modes.wide, by = "Species") %>% 
  select(Species, ".", `Marsh_Dry_Post-Invasion`, `Pond_Dry_Post-Invasion`) %>% 
  rename(`Marsh < Pond` = ".") %>% 
  distinct(Species, .keep_all = T) %>% 
  separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>% 
  pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>% 
  separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  pivot_wider(names_from = "Habitat", values_from = "Mode") %>% 
  mutate(#`Post > Post` = 1 - `Post < Post`,
    Direction = case_when(
      `Marsh < Pond` >= 0.95 ~ "Increase",
      `Marsh < Pond` < 0.05 ~ "Decrease",
      `Marsh < Pond` > 0.05 | `Marsh < Pond` < 0.95 ~ "No Change"),
    `Percent Change` = ((Pond-Marsh)/Marsh)*100,
    Species = case_when(
      #Species == "A_nat" ~ "A. natalis",
      #Species == "C_uro" ~ "M. urophthalmus",
      #Species == "E_eve" ~ "E. evergladei",
      #Species == "E_glo" ~ "E. gloriosus",
      Species == "F_chr" ~ "F. chrysotus",
      #Species == "F_con" ~ "F. confluentus",
      Species == "G_hol" ~ "G. holbrooki",
      Species == "H_for" ~ "H. formosa",
      Species == "H_lit" ~ "H. littorale", 
      #Species == "J_flo" ~ "J. floridae",
      Species == "L_goo" ~ "L. goodei",
      # Species == "L_gul" ~ "L. gulosus",
      #Species == "L_mac" ~ "L. macrochirus",
      Species == "L_mar" ~ "L. marginatus",
      #Species == "L_mic" ~ "L. microlophus",
      Species == "L_pun" ~ "L. punctatus"
      #Species == "L_pla" ~ "L. platyrhincus",
      #Species == "M_sal" ~ "M. salmoides",
      #Species == "N_gyr" ~ "N. gyrinus",
      #Species == "P_lat" ~ "P. latipinna"
      #Species == "T_mar" ~ "P. mariae"
    )) %>% 
  filter(!is.na(`Percent Change`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Invasion_Status, Season, Species, `Size Class`, Marsh, Pond, `Marsh < Pond`, 
         Direction, `Percent Change`)
```

#### Post-Invasion Wet vs Dry Marsh Comparisons
```{r}
#reformat data to have only Post-invasion wet-season pond-marsh comparisons
#diagonal will be comparisons within a size class
post.marsh.wet.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Post", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Marsh", Season == "Wet") %>% 
  arrange(Species) %>% 
  select(-c(Season)) %>% 
  unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Post", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  arrange(Species) %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Season)) %>% 
  unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>% 
  filter(Invasion_Habitat_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>%  
  rownames_to_column(var = "Invasion_Habitat_Spp") %>% 
  filter(Invasion_Habitat_Spp %in% colnames(.)) %>% 
  mutate(Season = "Dry") %>% 
  unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>% 
  column_to_rownames(var = "Season_Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>% 
  mutate(Season = "Wet") %>% 
  rownames_to_column(var = "Invasion_Habitat_Spp") %>% 
  unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>% 
  column_to_rownames(var = "Season_Invasion_Habitat_Spp")

#isolate comparisons within a spp size class
#reformat data
#calculate % change
post.marsh.wet.dry.direct.spp.pairwise <- post.marsh.wet.dry.direct.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(post.marsh.wet.dry.direct.pairwise)) %>% 
  separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>% 
  unite("Species", c(Genus, Species), sep = "_") %>% 
  merge(., pre.post.modes.wide, by = "Species") %>% 
  select(Species, ".", `Marsh_Wet_Post-Invasion`, `Marsh_Dry_Post-Invasion`) %>% 
  rename(`Wet < Dry` = ".") %>% 
  distinct(Species, .keep_all = T) %>% 
  separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>% 
  pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>% 
  separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  pivot_wider(names_from = "Season", values_from = "Mode") %>% 
  mutate(#`Post > Post` = 1 - `Post < Post`,
    Direction = case_when(
      `Wet < Dry` >= 0.95 ~ "Increase",
      `Wet < Dry` < 0.05 ~ "Decrease",
      `Wet < Dry` > 0.05 | `Wet < Dry` < 0.95 ~ "No Change"),
    `Percent Change` = ((Dry-Wet)/Wet)*100,
    Species = case_when(
      #Species == "A_nat" ~ "A. natalis",
      #Species == "C_var" ~ "C. variegatus",
      Species == "C_uro" ~ "M. urophthalmus",
      #Species == "E_eve" ~ "E. evergladei",
      Species == "E_suc" ~ "E. sucetta", 
      #Species == "E_glo" ~ "E. gloriosus",
      Species == "F_chr" ~ "F. chrysotus",
      #Species == "F_con" ~ "F. confluentus",
      Species == "G_hol" ~ "G. holbrooki",
      Species == "H_for" ~ "H. formosa",
      Species == "H_let" ~ "H. letourneuxi",
      #Species == "J_flo" ~ "J. floridae",
      Species == "L_goo" ~ "L. goodei",
      #Species == "L_gul" ~ "L. gulosus",
      #Species == "L_mac" ~ "L. macrochirus",
      #Species == "L_mar" ~ "L. marginatus",
      #Species == "L_mic" ~ "L. microlophus",
      Species == "L_pun" ~ "L. punctatus"
      #Species == "L_pla" ~ "L. platyrhincus",
      #Species == "M_sal" ~ "M. salmoides",
      #Species == "N_gyr" ~ "N. gyrinus",
      #Species == "P_lat" ~ "P. latipinna"
      #Species == "T_mar" ~ "P. mariae"
    )) %>% 
  filter(!is.na(`Percent Change`)) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  arrange(Species) %>% 
  select(Invasion_Status, Habitat, Species, `Size Class`, Wet, Dry, `Wet < Dry`, 
         Direction, `Percent Change`)
```

#### Post-Invasion Wet vs Dry Pond Comparisons
```{r}
#reformat data to have only Post-invasion wet-season pond-Pond comparisons
#diagonal will be comparisons within a size class
post.pond.wet.dry.direct.pairwise <- pre.post.pairwise %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Post", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Pond", Season == "Wet") %>% 
  arrange(Species) %>% 
  select(-c(Season)) %>% 
  unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Invasion_Status_Spp") %>% 
  filter(grepl("Post", Invasion_Status_Spp)) %>% 
  separate(Invasion_Status_Spp, into = c("Invasion_Status", "Species"), sep = "[//.]") %>% 
  arrange(Species) %>% 
  mutate(Invasion_Status  = gsub("\\[[[0-9]+] ", "", Invasion_Status)) %>% 
  separate(Invasion_Status, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  filter(Habitat == "Pond", Season == "Dry") %>% 
  select(-c(Season)) %>% 
  unite("Invasion_Habitat_Spp", c(Invasion_Status, Habitat, Species), sep = "_") %>% 
  filter(Invasion_Habitat_Spp %in% colnames(.)) %>% 
  column_to_rownames(var = "Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>%  
  rownames_to_column(var = "Invasion_Habitat_Spp") %>% 
  filter(Invasion_Habitat_Spp %in% colnames(.)) %>% 
  mutate(Season = "Dry") %>% 
  unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>% 
  column_to_rownames(var = "Season_Invasion_Habitat_Spp") %>% 
  t() %>% as.data.frame() %>% 
  mutate(Season = "Wet") %>% 
  rownames_to_column(var = "Invasion_Habitat_Spp") %>% 
  unite("Season_Invasion_Habitat_Spp", c(Season, Invasion_Habitat_Spp)) %>% 
  column_to_rownames(var = "Season_Invasion_Habitat_Spp")

#isolate comparisons within a spp size class
#reformat data
#calculate % change
post.pond.wet.dry.direct.spp.pairwise <- post.pond.wet.dry.direct.pairwise %>% as.matrix() %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(post.pond.wet.dry.direct.pairwise)) %>% 
  separate(Species, into = c("Habitat", "Invasion_Status", "Season", "Genus", "Species"), sep = "_") %>% 
  unite("Species", c(Genus, Species), sep = "_") %>% 
  merge(., pre.post.modes.wide, by = "Species") %>% 
  select(Species, ".", `Pond_Wet_Post-Invasion`, `Pond_Dry_Post-Invasion`) %>% 
  rename(`Wet < Dry` = ".") %>% 
  distinct(Species, .keep_all = T) %>% 
  separate(Species, into = c("Species", "Size Class"), sep = "(?<=[A-Za-z])(?=[0-9])") %>% 
  pivot_longer(cols = contains("Invasion"), names_to = "Habitat_Season_Invasion", values_to = "Mode") %>% 
  separate(Habitat_Season_Invasion, into = c("Habitat", "Season", "Invasion_Status")) %>% 
  pivot_wider(names_from = "Season", values_from = "Mode") %>% 
  mutate(#`Post > Post` = 1 - `Post < Post`,
    Direction = case_when(
      `Wet < Dry` >= 0.95 ~ "Increase",
      `Wet < Dry` < 0.05 ~ "Decrease",
      `Wet < Dry` > 0.05 | `Wet < Dry` < 0.95 ~ "No Change"),
    `Percent Change` = ((Dry-Wet)/Wet)*100,
    Species = case_when(
      #Species == "A_nat" ~ "A. natalis",
      #Species == "B_bel" ~ "B. belizanus",
      #Species == "C_bat" ~ "C. batrachus",
      #Species == "C_var" ~ "C. variegatus",
      #Species == "C_uro" ~ "M. urophthalmus",
      #Species == "E_eve" ~ "E. evergladei",
      #Species == "E_glo" ~ "E. gloriosus",
      Species == "F_chr" ~ "F. chrysotus",
      #Species == "F_con" ~ "F. confluentus",
      Species == "G_hol" ~ "G. holbrooki",
      Species == "H_for" ~ "H. formosa",
      #Species == "J_flo" ~ "J. floridae",
      Species == "L_goo" ~ "L. goodei",
      Species == "L_gul" ~ "L. gulosus",
      #Species == "L_mac" ~ "L. macrochirus",
      Species == "L_mar" ~ "L. marginatus",
      #Species == "L_mic" ~ "L. microlophus",
      Species == "L_pun" ~ "L. punctatus",
      Species == "L_pla" ~ "L. platyrhincus",
      #Species == "M_sal" ~ "M. salmoides",
      #Species == "N_gyr" ~ "N. gyrinus"
      Species == "P_lat" ~ "P. latipinna"
      #Species == "T_mar" ~ "P. mariae"
    )) %>% 
  filter(!is.na(`Percent Change`)) %>% 
  arrange(Species) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  select(Invasion_Status, Habitat, Species, `Size Class`, Wet, Dry, `Wet < Dry`, 
         Direction, `Percent Change`)

```

### Plot Pre- vs Post-Invasion 
#### Trophic Niche Ellipses

Reformat data for plotting
```{r}
#reformat data to be able to make boxplots or violoin plots
#also isolating species size classes from habitat-season levels that are present both pre- and post-invasion
pre.post.plot.species <- pre.post.direct.spp.pairwise %>% 
  mutate(Species_Abr = case_when(
    Species == "A. natalis" ~ "A_nat",
    Species == "M. urophthalmus" ~ "C_uro",
    Species == "E. evergladei" ~ "E_eve",
    Species == "E. gloriosus" ~ "E_glo",
    Species == "F. chrysotus" ~ "F_chr",
    Species == "G. holbrooki" ~ "G_hol",
    Species == "H. formosa" ~ "H_for",
    Species == "J. floridae" ~ "J_flo",
    Species == "L. goodei" ~ "L_goo",
    Species == "L. gulosus" ~ "L_gul",
    Species == "L. macrochirus" ~ "L_mac",
    Species =="L. marginatus" ~ "L_mar",
    Species == "L. microlophus" ~ "L_mic",
    Species == "L. punctatus" ~ "L_pun",
    Species == "L. platyrhincus" ~ "L_pla",
    Species == "M. salmoides" ~ "M_sal",
    Species == "P. latipinna" ~ "P_lat",
    Species == "P. mariae" ~"T_mar")) %>% 
  unite("Habitat_Season", c(Habitat, Season), sep = "_") %>% 
  unite("Spp_Class", c(Species_Abr, `Size Class`), sep = "") %>% 
  unite("Plot_Levels", c(Habitat_Season, Spp_Class), sep = ".")

pre.post.plot.data <- pre.post.SEA.B %>% as.data.frame() %>% 
  pivot_longer(everything(), names_to = c("Invasion Status", "Species"), 
               names_sep = "[//.]", values_to = "SEA_B") %>% 
  separate(`Invasion Status`, into = c("Habitat", "Season", "Invasion Status"), "_") %>% 
  unite("Habitat_Season", c(Habitat, Season), sep = "_") %>% 
  unite(Habitat_Season_Spp, c(Habitat_Season, Species), sep = ".") %>% 
  filter(Habitat_Season_Spp %in% pre.post.plot.species$Plot_Levels) %>% 
  separate(Habitat_Season_Spp, into = c("Habitat_Season", "Species"), sep = "[//.]") %>% 
  separate(Habitat_Season, into = c("Habitat", "Season"), sep = "_")

```

Box plots
```{r}
#plot these data
windowsFonts(Times = windowsFont("Times New Roman"))

(pre.post.seab<-ggplot(pre.post.plot.data, aes(x = Species, y = SEA_B, fill = `Invasion Status`))+
    geom_boxplot(outlier.size = 0,outlier.alpha = 0, lwd = 0.1, alpha = 0.5)+
    facet_grid(Habitat~Season, scales = "free_x")+
    scale_color_viridis_d(aesthetics = "fill", option = "D", end = 0.75)+
    scale_y_continuous(limits = c(0, 2.5), expand = c(0.01, 0))+
    theme_bw(base_size = 12)+
    xlab(NULL)+
    ylab(expression("Stomach Content Niche Area - SEA"["b"]))+
    theme(text = element_text(family = "Times", size = 12),
          axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),
          #panel.border = element_blank(), 
          #panel.grid.major = element_blank(),
          #panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black"),
          axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),
          axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_blank(), legend.text = element_text(size = 8),
          legend.key.size = unit(0.75, "line"))
  
)

ggsave(filename = "Figures/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAb.tiff", 
       plot = pre.post.seab, 
       dpi = 300, width = 200, height = 100, units = "mm")
```

#### Frequency Histogram of Effect Sizes
##### Invasion Effect Sizes
Reformat data for plotting
```{r}
pre.post.stat.diff.guild <- pre.post.direct.spp.pairwise %>% 
  #filter(`Pre < Post` >= 0.95 | `Pre < Post` <= 0.05) %>% 
  mutate(Guild = case_when(
    Species %in% c("P. latipinna", "P. mariae") ~ "Detritivore",
    Species %in% c("E. evergladei", "E. gloriosus", "F. chrysotus", "L. goodei", "L. macrochirus",
                   "L. marginatus", "L. microlophus") ~ "Invertivore",
    Species == "L. gulosus" ~ "Mesopredator",
    Species %in% c("M. salmoides", "L. platyrhincus") ~ "Top Predator",
    Species %in% c("A. natalis", "G. holbrooki", "H. formosa", "J. floridae", "L. punctatus", 
                   "M. urophthalmus") ~ "Omnivore"
  ),
  `Statistically Different` = case_when(
    `Pre < Post` >= 0.95 | `Pre < Post` <= 0.05 ~ "Yes",
    T ~ "No"
  ))

```

Generate Plot
```{r}
windowsFonts(Times = windowsFont("Times New Roman"))

(pre.post.stat.diff.freq.hist <- ggplot(data = pre.post.stat.diff.guild, 
                                        aes(x = `Percent Change`, fill = Guild, color = `Statistically Different`))+
    geom_histogram(bins = 20)+
    facet_grid(Habitat ~ Season, scales = "free")+
    scale_fill_viridis_d(option = "C")+
    scale_color_viridis_d(option = "B", direction = -1)+
    scale_y_continuous(expand = c(0,0))+
    #scale_x_continuous()
    ylab("Number of Species Size Classes")+
    xlab("Trophic Dispersion - Percent Change in Trophic Niche Area")+
    theme_bw(base_size = 12)+
    theme(text = element_text(family = "Times", size = 12),
          #axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 4),
          #panel.border = element_blank(), 
          #panel.grid.major = element_blank(),
          #panel.grid.minor = element_blank(), 
          axis.line = element_line(colour = "black"),
          axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 12), axis.text.x.bottom = element_text(size = 7),
          axis.text.y = element_text(size = 7), axis.ticks.y = element_line(size = 0.2),
          #legend.title = element_blank(), 
          legend.text = element_text(size = 8)#,
          #legend.key.size = unit(0.75, "line")
          )

)

ggsave(filename = "Figures/Stomach Contents/Trophic Niche/Pre-vs-Post-Invasion/pre_post_SEAb_effect_size_freq_hist.tiff", 
       plot = pre.post.stat.diff.freq.hist, 
       dpi = 300, width = 200, height = 100, units = "mm")
```

##### Pre/Post Habitat Shift Effect Sizes
```{r}
pre.post.habitat.shifts <- bind_rows(pre.marsh.pond.wet.direct.spp.pairwise,
                                     pre.marsh.pond.Dry.direct.spp.pairwise,
                                     post.marsh.pond.wet.direct.spp.pairwise,
                                     post.marsh.pond.dry.direct.spp.pairwise) %>% 
  mutate(Guild = case_when(
    Species %in% c("P. latipinna", "P. mariae", "O. aureus") ~ "Detritivore",
    Species %in% c("E. evergladei", "E. gloriosus", "F. chrysotus", "L. goodei", "L. macrochirus",
                   "L. marginatus", "L. microlophus", "E. sucetta", "F. confluentus") ~ "Invertivore",
    Species == "L. gulosus" ~ "Mesopredator",
    Species %in% c("M. salmoides", "L. platyrhincus") ~ "Top Predator",
    Species %in% c("A. natalis", "G. holbrooki", "H. formosa", "J. floridae", "L. punctatus", 
                   "M. urophthalmus", "N. gyrinus", "H. letourneuxi", "H. littorale") ~ "Omnivore"
  )) %>% 
  mutate(Invasion_Status = case_when(
    Invasion_Status == "Pre" ~ "Pre-Invasion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